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he rapid development of artificial intelligence and robots, the research and development of intelligent dialogue robots are very necessary for today's society. However, a robotic algorithm system that simulates navigational English conversation has not yet been developed. In order to find a suitable algorithm system for dialogue robots, this paper uses the test data set to test the analytical model of navigational English dialogue instructions. The experimental results show that the conditional random field (CRF) + domain dictionary + ambiguity resolution method has the highest segmentation effect. The calculated percentages of the analytical model are correct rate: 76.85%; recall rate: 80.36%; F-value: 88.46%. This paper implements a robot teaching and reproduction method based on simulated navigational English conversation and human computer interaction under the background of artificial intelligence, and designs robot motion realization experiments and speech recognition experiments. The three-dimensional error after fine-tuning the voice is between 1.6798mm and 2.9968mm. This article constructs a simulation navigational English dialogue robot system. The FAQ component has up to 79.2%; others have a lower accuracy rate of only 59.03%. [ABSTRACT FROM AUTHOR]

Multiple images have been widely used for scene understanding and navigation of unmanned ground vehicles in long term operations. However, as the amount of visual data in multiple images is huge, the cumulative error in many cases becomes untenable. This paper proposes a novel method that can extract features from a large dataset of multiple images efficiently. Then the membership ${K}$ -means clustering is used for high dimensional features, and the large dataset is divided into ${N}$ subdatasets to train ${N}$ conditional random field (CRF) models based on superpixel. A Softmax subdataset selector is used to decide which one of the ${N}$ CRF models is chosen as the prediction model for labeling images. Furthermore, some experiments are conducted to evaluate the feasibility and performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Using mobile Light Detection and Ranging point clouds to accomplish road scene labeling tasks shows promise for a variety of applications. Most existing methods for semantic labeling of point clouds require a huge number of fully supervised point cloud scenes, where each point needs to be manually annotated with a specific category. Manually annotating each point in point cloud scenes is labor intensive and hinders practical usage of those methods. To alleviate such a huge burden of manual annotation, in this paper, we introduce an active learning method that avoids annotating the whole point cloud scenes by iteratively annotating a small portion of unlabeled supervoxels and creating a minimal manually annotated training set. In order to avoid the biased sampling existing in traditional active learning methods, a neighbor-consistency prior is exploited to select the potentially misclassified samples into the training set to improve the accuracy of the statistical model. Furthermore, lots of methods only consider short-range contextual information to conduct semantic labeling tasks, but ignore the long-range contexts among local variables. In this paper, we use a higher order Markov random field model to take into account more contexts for refining the labeling results, despite of lacking fully supervised scenes. Evaluations on three data sets show that our proposed framework achieves a high accuracy in labeling point clouds although only a small portion of labels is provided. Moreover, comparative experiments demonstrate that our proposed framework is superior to traditional sampling methods and exhibits comparable performance to those fully supervised models. [ABSTRACT FROM AUTHOR]

This paper presents a novel tracking-by-detection approach for multitarget tracking. There are two major steps in our framework: data association to form global tracklet association, followed by trajectory estimation to deal with the remaining gaps. In the first step, we formulate tracklet association as an inference problem in a Hough forest random field, which combines Hough forest and conditional random field and allows us to model both local and global tracklet relationships in one unified model. In the second step, we improve the reversible-jump Markov chain Monte Carlo particle filtering method with explicit mutual-occlusion reasoning to fill in the remaining gaps from the first step and increase the overall tracking precision. Extensive experiments have been conducted on five public data sets, and the performance is comparable to that of the state-of-the-art method, if not better. [ABSTRACT FROM AUTHOR]

The semantic segmentation of remote sensing images is a significant research direction in digital image processing. The complex background environment, irregular size and shape of objects, and similar appearance of different categories of remote sensing images have brought great challenges to remote sensing image segmentation tasks. Traditional convolutional-neural-network-based models often ignore spatial information in the feature extraction stage and pay less attention to global context information. However, spatial context information is important in complex remote sensing images, which means that the segmentation effect of traditional models needs to be improved. In addition, neural networks with a superior segmentation performance often suffer from the problem of high computational resource consumption. To address the above issues, this paper proposes a combination model of a modified multiscale deformable convolutional neural network (mmsDCNN) and dense conditional random field (DenseCRF). Firstly, we designed a lightweight multiscale deformable convolutional network (mmsDCNN) with a large receptive field to generate a preliminary prediction probability map at each pixel. The output of the mmsDCNN model is a coarse segmentation result map, which has the same size as the input image. In addition, the preliminary segmentation result map contains rich multiscale features. Then, the multi-level DenseCRF model based on the superpixel level and the pixel level is proposed, which can make full use of the context information of the image at different levels and further optimize the rough segmentation result of mmsDCNN. To be specific, we converted the pixel-level preliminary probability map into a superpixel-level predicted probability map according to the simple linear iterative clustering (SILC) algorithm and defined the potential function of the DenseCRF model based on this. Furthermore, we added the pixel-level potential function constraint term to the superpixel-based Gaussian potential function to obtain a combined Gaussian potential function, which enabled our model to consider the features of various scales and prevent poor superpixel segmentation results from affecting the final result. To restore the contour of the object more clearly, we utilized the Sketch token edge detection algorithm to extract the edge contour features of the image and fused them into the potential function of the DenseCRF model. Finally, extensive experiments on the Potsdam and Vaihingen datasets demonstrated that the proposed model exhibited significant advantages compared to the current state-of-the-art models. [ABSTRACT FROM AUTHOR]

Chinese named entity recognition plays an important role in downstream tasks such as machine translation and intelligent question answering. A new Chinese named entity recognition algorithm based on gazetteers and syntactic dependency tree is proposed in this paper. To solve the problem of error transmission caused by the lack of word information in character vector and syntactic dependent structure information between words. This algorithm forms a graph of the gazetteers information and the syntactic dependency tree information in the sentence and then integrates it into the character vector through adaptive gated graph neural networks (AGGNN), so that the semantic relationship between words is obtained well in each character vector and the recognition accuracy is improved. Through the verification in Ecommerce, Resume, QI and other data sets, the new method can greatly improve the accuracy of Chinese entity recognition. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for the multitemporal and contextual classification of georeferenced optical remote sensing images acquired at different epochs and having different geometrical resolutions. The method is based on Conditional Random Fields (CRFs) for contextual classification. The CRF model is expanded by temporal interaction terms that link neighboring epochs via transition probabilities between different classes. In order to be able to deal with data of different resolution, the class structure at different epochs may vary with the resolution. The goal of the multitemporal classification is an improved classification performance at all individual epochs, but also the detection of land-cover changes, possibly using lower resolution data. This paper also contains a comparison of the performance of different models for the interaction potentials. Results are given for two different test sites in Germany, where Ikonos, RapidEye, and Landsat images are available. Our results show that the multitemporal classification does indeed increase the overall accuracy of all epochs compared to a monotemporal classification and to a state-of-the-art multitemporal classification method, and that it is feasible to detect changes in lower resolution images. [ABSTRACT FROM PUBLISHER]

Automatically identifying characters in movies has attracted researchers’ interest and led to several significant and interesting applications. However, due to the vast variation in character appearance as well as the weakness and ambiguity of available annotation, it is still a challenging problem. In this paper, we investigate this problem with the supervision of actor-character name correspondence provided by the movie cast. Our proposed framework, namely, Cast2Face, is featured by: 1) we restrict the assigned names within the set of character names in the cast; 2) for each character, by using the corresponding actor and movie name as keywords, we retrieve from the Google image search and get a group of face images to form the gallery set; 3) the probe face tracks in the movie are then identified as one of the actors by a robust kernel multitask joint sparse representation and classification method; and 4) the conditional random field model with consideration of the constraints between face tracks is introduced to enhance the final labeling. Finally, the assigned actor name of a face track is then mapped to the character name based on the cast again. Besides face naming, we further apply the proposed method to spotlight the summarization of a particular actor in his/her movies. We conduct extensive experiments and empirical evaluations on several feature-length movies to demonstrate the satisfying performance of our method. [ABSTRACT FROM AUTHOR]

Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method. [ABSTRACT FROM AUTHOR]

Feature selection is an important task in hyperspectral data analysis. This paper presents a sparse conditional random field (SCRF) model to select relevant features for the classification of hyperspectral images and, meanwhile, to exploit the contextual information in the form of spatial dependences in the images. The sparsity arises from the use of a Laplacian prior on the CRF parameters, which encourages the parameter estimates to be either significantly large or exactly zero. To joint the feature selection and classifier design, this paper develops an efficient sparse training method, which divides the training of SCRF into the sparse trainings of two simpler classifiers. Experiments on the real-world hyperspectral image attest to the accuracy, sparsity, and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Each pixel can be classified in the image by the semantic segmentation. The segmentation detection results of pixel level can be got which are similar to the contour of the target object. However, the results of semantic segmentation trained by Fully convolutional networks often lead to the loss of detail information. This paper proposes a CRF-FCN model based on CRF optimization. Firstly, the original image is detected based on feature pyramid networks, and the target area information is extracted, which is used to train the high-order potential function of CRF. Then, the high-order CRF is used as the back-end of the complete convolution network to optimize the semantic image segmentation. The algorithm comparison experiment shows that our algorithm makes the target details more obvious, and improves the accuracy and efficiency of semantic segmentation. [ABSTRACT FROM AUTHOR]

A high-order conditional random field (CRF) for depth estimation from a single image is proposed in this paper. Instead of formulating the problem with the Guassian or Laplacian CRF modeling techniques, which cannot exploit the full potential offered by the probabilistic modeling, this paper proposes a depth estimation CRF model with field of experts (FoE) as the prior. The minimum mean square error (MMSE) criteria is used to infer depth. Moreover, it is assumed that the variance of depth estimation error varies spatially in depth estimation model. This allows the proposed method to enjoy the benefits offered by the flexible prior and have the advantages of making use of the non-stationary variance probability model. Experimental results indicate that the proposed method outperforms state-of-the-art approaches in terms of RMSE-error and log10-error. [ABSTRACT FROM AUTHOR]

The task of semantic segmentation is to infer a predefined category label for each pixel in the image. For most cases, image segmentation is established as a fully supervised task. These methods all built on the basis of having access to sufficient pixel-wise annotated samples for training. However, obtaining the satisfied ground truth is not only labor intensive but also time-consuming, which severely hinders the generality of these fully supervised methods. Instead of pixel-level ground truth, weakly supervised approaches learn their models from much less prior information, e.g., image-level annotation. In this paper, we propose a novel conditional random field (CRF) based framework for weakly supervised semantic segmentation. Enlightened by jigsaw puzzles, we start the approach with merging superpixels from an image into larger pieces by a newly designed strategy. Then pieces from all the training images are gathered and associated with appropriate semantic labels by CRF. Thus, the piece library is constructed, achieving remarkable universality and flexibility. In the case of testing, we compare the superpixels with image pieces in the library and assign them the labels that minimize the potential energy. In addition, the proposed framework is fit for domain adaption and obtains promising results, which is of great practical value. Extensive experimental results on PASCAL VOC 2007, MSRC-21, and VOC 2012 databases demonstrate that our framework outperforms or is comparable to state-of-the-art segmentation methods. [ABSTRACT FROM AUTHOR]

Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. The recently defined conditional random field (CRF) can effectively model and use the dependencies for classification of hyperspectral images in a unified probabilistic framework. However, in order to be computationally tractable, the usual CRFs are limited to incorporate only pairwise potentials. Thus, the usual CRFs can capture only pairwise interactions and neglect higher order dependencies, which are potentially useful high-level properties particularly for the classification of hyperspectral image consisting of complex components. This paper overcomes this limitation by developing hyperspectral image classification algorithm based on a CRF with sparse higher order potentials, which are specially designed to incorporate complex characteristics of hyperspectral images. To efficiently implement the CRF model at training step, this paper develops an efficient local method under the piecewise training framework, while at inference step, this proposes a simple strategy to combine the piecewisely trained model to overcome the possible over-counting problems. Moreover, the combined model with the specially defined potentials can be efficiently inferred by graph cut method. Experiments on the real-world data attest to the accuracy, effectiveness, and efficiency of the proposed model on modeling and classifying hyperspectral images. [ABSTRACT FROM AUTHOR]

Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. State-of-the-art hyperspectral image classification algorithms use the dependencies in a heuristic way or in probabilistic frameworks but impose unreasonable assumptions on observed data. In this paper, we formulate a conditional random field (CRF) to replace such heuristics and unreasonable assumptions for the classification of hyperspectral images. Moreover, because of avoiding explicit modeling of the observed data, the proposed method can incorporate the classification of hyperspectral images with different statistics characteristics into a unified probabilistic framework. Since the usual classification task for hyperspectral images needs the proposed CRF to be trained on local samples, available global training methods cannot be directly used. Under piecewise training framework, this paper develops an efficient local method to train the CRF. It is efficiently implemented through separated trainings of simple classifiers defined by corresponding potentials. However, the independent classifier trainings may lead to over-counting problems during inference. So we further propose a strategy to combine the independently trained models to obtain final CRF model. Experiments on real-world hyperspectral data show that our algorithm is competitive with the most recent results in hyperspectral image classification. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet has penetrated into all aspects of people's lives. A large number of online customer reviews have been accumulated in several product forums, which are valuable resources to be analyzed. However, these customer reviews are unstructured textual data, in which a lot of ambiguities exist, so analyzing them is a challenging task. At present, the effective deep semantic or fine-grained analysis of customer reviews is rare in the existing literature, and the analysis quality of most studies is also low. Therefore, in this paper a fine-grained opinion mining method is introduced to extract the detailed semantic information of opinions from multiple perspectives and aspects from Chinese automobile reviews. The conditional random field (CRF) model is used in this method, in which semantic roles are divided into two groups. One group relates to the objects being reviewed, which includes the roles of manufacturer, the brand, the type, and the aspects of cars. The other group of semantic roles is about the opinions of the objects, which includes the sentiment description, the aspect value, the conditions of opinions and the sentiment tendency. The overall framework of the method includes three major steps. The first step distinguishes the relevant sentences with the irrelevant sentences in the reviews. At the second step the relevant sentences are further classified into different aspects. At the third step fine-grained semantic roles are extracted from sentences of each aspect. The data used in the training process is manually annotated in fine granularity of semantic roles. The features used in this CRF model include basic word features, part-of-speech (POS) features, position features and dependency syntactic features. Different combinations of these features are investigated. Experimental results are analyzed and future directions are discussed. [ABSTRACT FROM AUTHOR]

This paper proposed a construction framework of pet knowledge graph. It designed and constructed the schema layer in a top-down manner and constructed the data layer by extracting knowledge from semi-structured and unstructured data. For entity extraction of unstructured data, this paper proposed a symptom-named entity recognition method which combined conditional random field (CRF) and pet symptom dictionary. The method used symptom dictionary to identify the text and obtained the semantic category information, and then combined CRF and the semantic information to identify symptom-named entities. The experimental results show the effectiveness of the method. It selected the attribute graph model supported by the OrientDB database for knowledge representation. The knowledge graph used the OrientDB graph database for knowledge storage. In addition, it showed the constructed pet knowledge graph. [ABSTRACT FROM AUTHOR]

With complex building composition and imaging condition, urban areas show versatile characteristics in remote sensing optical images. It demonstrates that multiple features should be utilized to characterize urban areas. On the other hand, since levels of development in neighboring areas are not statistically independent, the features of each urban area site depend on those of neighboring sites. In this paper, we present a multiple conditional random fields (CRFs) ensemble model to incorporate multiple features and learn their contextual information. This model involves two aspects: one is to use a CRF as the base classifier to automatically generate a set of CRFs by changing input features, and the other is to integrate the set of CRFs by defining a conditional distribution. The model has some distinct merits: each CRF component models a kind of feature, so that the ensemble model can learn different aspects of training data. Moreover, it lets the ensemble model search in a wide solution space. The ensemble model can also avoid the well-known overfitting problem of a single CRF, i.e., the many features may cause the redundancy of irrelevant information and result in counter-effect. Experiments on a wide range of images show that our ensemble model produces higher detection accuracy than single CRF and is also competitive with recent results in urban area detection. [ABSTRACT FROM AUTHOR]

In this paper, a novel change detection method based on conditional random field (CRF) with region connection constraints in multitemporal high-resolution remote sensing images is proposed. The change detection problem is formulated as a labeling issue to discriminate the changed class from the unchanged class in the difference image. In the CRF model, the unary potential is described by using the memberships of unsupervised fuzzy C-means clustering algorithm. The pairwise potential adopts a boundary constraint based on Euclidean distance. In addition, region iteration potential defined on a set of pixels is incorporated into CRF model to suppress the oversmooth performance. A chief advantage of our approach is to be able to achieve correct change map and avoid training a large number of model parameters. Experimental results demonstrate that the proposed method improves the change detection accuracy, is more robust against noise than other state-of-the-art approaches, and preserves boundary information. [ABSTRACT FROM PUBLISHER]

Recent research shows that the security performance of JPEG image steganography can be improved by developing an accurate statistical model to characterize the correlations between Discrete Cosine Transform (DCT) coefficients. In this paper, a novel JPEG steganographic scheme called J-CRF is proposed to characterize the correlations between DCT coefficients using a pairwise Conditional Random Field (CRF) model, in which the correlated DCT coefficient pairs are modeled as bivariate Gaussian random variables. Under the framework of pairwise CRF, the proposed scheme formulates the problem of minimizing the statistical detectability within the Gaussian image model as the minimization of the sum of a series of unary and pairwise potentials, both of which are utilized to measure the local detectability of embedding messages in the DCT coefficient pairs. All the potentials associated with statistical detectability are formalized as the KL divergence between the statistical distributions of the cover and the stego. Experimental results demonstrate that the proposed J-CRF can outperform other SOTA JPEG steganographic schemes, e.g., J-UNIWARD, J-MiPOD, and JEC-RL, in resisting the detection of various advanced steganalyzers. In addition, the performance of J-CRF could be further boosted by reformulating it as the weighted sum of unary and pairwise potentials, called J-CRFW. • Propose a novel JPEG steganographic scheme J-CRF using Conditional Random Field (CRF). • Characterize the correlations between DCT coefficients using pairwise CRF. • Cast the task of JPEG steganography as minimizing the detectability with CRF. • Improve J-CRF by reformulating the sum of potentials in a weighted manner. • Demonstrate superior security performance over other SOTA schemes. [ABSTRACT FROM AUTHOR]

With the widespread application of deep learning in the field of computer vision, gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance. Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction, model over-fitting, and low degree of semantic information fusion, an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images. In this model, we make full use of the residual network (ResNet) to solve the over-fitting problem. In order to process and aggregate data at different scales, the inception network is used instead of the traditional convolutional layer, and the dilated convolution is used to increase the receptive field. The conditional random field ( CRF) can complete the contour refinement work. Compared with the traditional 3D U-Net network, the segmentation accuracy of the improved liver and tumor images increases by 2.89% and 7.66%, respectively. As a part of the image processing process, the method in this paper not only can be used for medical image segmentation, but also can lay the foundation for subsequent image 3D reconstruction work. [ABSTRACT FROM AUTHOR]

Part of speech (POS) tagging, the assignment of syntactic categories for words in running text, is significant to natural language processing as a preliminary task in applications such as speech processing, information extraction, and others. Urdu language processing presents a challenge due to the dual behaviour of various Urdu POS tags in differing situations (morphosyntactic ambiguity). This paper addresses this challenge by developing a novel tagging approach using linear-chain conditional random fields (CRF). Our work is the first instance of a CRF approach for Urdu POS tagging. The proposed model employs a strong, stable and balanced language-independent as well as language dependent feature set. The language-dependent feature considered includes part-of-speech tag of the previous word and suffix of the current word while the language-independent features includes the 'context words window'. Our approach was evaluated against support vector machine techniques for Urdu POS—considered as state of the art—on two benchmark datasets. The results show our CRF approach to improve upon the F-measure of prior attempts by 8.3–8.5%. [ABSTRACT FROM AUTHOR]

Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps. [ABSTRACT FROM PUBLISHER]

While electric vehicles (EVs) are expected to provide environmental and economical benefit, judicious coordination of EV charging is necessary to prevent overloading of the distribution grid. Leveraging the smart grid infrastructure, the utility company can adjust the electricity price intelligently for individual customers to elicit desirable load curves. In this context, this paper addresses the problem of predicting the EV charging behavior of the consumers at different prices, which is a prerequisite for optimal price adjustment. The dependencies on price responsiveness among consumers are captured by a conditional random field (CRF) model. To account for temporal dynamics potentially in a strategic setting, the framework of online convex optimization is adopted to develop an efficient online algorithm for tracking the CRF parameters. The proposed model is then used as an input to a stochastic profit maximization module for real-time price setting. Numerical tests using simulated and semi-real data verify the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

With the complex building composition and imaging condition, urban areas show versatile characteristics in remote sensing images. In the literature of land-cover analysis, many algorithms utilize the features with structural information to characterize urban areas. Typically, these are more successful on some types of imagery than others, since they usually use only one kind or a few kinds of structural information. On the other hand, since levels of development in neighboring areas are not statistically independent, the multiple features (encoding the multilevel structural information) of each site in urban area depend on that of neighboring sites. In this paper, a new-come discriminative model, i.e., conditional random field (CRF), is introduced to learn the dependencies and fuse the multilevel structural information to obtain the essential detection. To meet the higher needs of some users, we introduce a two-component-based Markov random field model and show how to integrate it tightly with CRF model to refine the results from essential detection. Experiments on a wide range of images show that our algorithms are competitive with recent results in urban area detection. [ABSTRACT FROM AUTHOR]

A new change-detection method for remote sensing images based on a conditional random field (CRF) model is proposed in this paper. The method artfully uses memberships of Fuzzy C-means as unary potentials in the fully connected CRF (FCCRF) model without training parameters, and pairwise potentials of the CRF model are defined by a linear combination of Gaussian kernels, with which a highly efficient approximate inference algorithm can be used. The proposed FCCRF model is expressed on the complete set of pixels in both the observed multitemporal images, which can incorporate long range contextual information of remote-sensing images and enable greatly refined change-detection results. Experimental results demonstrate that the proposed approach leads to more accurate pixel-level change-detection performance and is more robust against noise than traditional algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel technique for unsupervised change detection in high spatial remote sensing images based on a conditional random field (CRF) model. The change-detection problem is formulated as a labeling issue to discriminate the changed class from the unchanged class in the difference image. CRF which employs the spatial property on both pixel's spectral data and labels have been widely used in many remote sensing applications. However, as there are a large number of model parameters to train, the CRF-based change-detection approach is time consuming and difficult to implement. The proposed method artfully uses memberships of Fuzzy C-means as unary potentials and defines pairwise potentials using a scaled squared Euclidean distance between neighboring pixels. This not only avoids training parameters but also helps improving the accuracy and the degree of automation. The experimental results obtained from three different remote sensing images demonstrate the accuracy and efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm for license plate detection in complex scenes, particularly for the all-day traffic surveillance environment. Unlike low-level feature-based methods, our work is motivated by component-based models for object detection. The detection process is divided into three steps, namely, decomposition, modeling, and inference. First, observing that one license plate is decomposed into several constituent characters, the maximally stable extremal region detector is used to extract candidate characters in images. Then, conditional random field (CRF) models are constructed on the candidate characters in neighborhoods. This way, the spatial and visual relationships among the characters is integrated in CRF in the form of probability distribution. Finally, the exact bounding boxes of license plates are estimated through the belief propagation inference on CRF. Both visual and structural features of license plates are fully exploited during detection. Hence, our approach can adapt to various environmental factors, such as cluttered background and illumination variation. A series of experiments are conducted on images that are collected from the actual road surveillance environment. The experimental results show the outstanding detection performance of the proposed method comparing with traditional algorithms. [ABSTRACT FROM PUBLISHER]

This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory VOE results. [ABSTRACT FROM PUBLISHER]

Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the strong dependencies across spatial and spectral neighbors have been proved to be very useful. Many available hyperspectral image denoising algorithms adopt multidimensional tools to deal with the problems and thus naturally focus on the use of the spectral dependencies. However, few of them were specifically designed to use the spatial dependencies. In this paper, we propose a multiple-spectral-band CRF (MSB-CRF) to simultaneously model and use the spatial and spectral dependencies in a unified probabilistic framework. Furthermore, under the proposed MSB-CRF framework, we develop two hyperspectral image denoising algorithms, which, thanks to the incorporated spatial and spectral dependencies, can significantly remove the noise, while maintaining the important image details. The experiments are conducted in both simulated and real noisy conditions to test the proposed denoising algorithms, which are shown to outperform the popular denoising methods described in the previous literatures. [ABSTRACT FROM PUBLISHER]

Hidden Markov Model (HMM) is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human-Robot Interaction (HRI). In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2-stages Hidden Markov Model. The 1st HMM is to recognize the prime command-like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance. [ABSTRACT FROM AUTHOR]

Relation extraction is an important task in information extraction. While dealing with the question-answer pairs, in addition to the relations among the entities in the texts, the question pattern as the relation connected questions and the answers also needs to he extracted. The combination of the supervised labeling algorithm(conditional random field) and the semi-super-vised algorithm based on a feature template! Bootstrapping) has a good performance when extracting relationships between entities. However,the method to find the template in the traditional semi-supervised algorithm is hard to move to the extraction of the question pattern. Therefore, this paper proposed model based on the combination of sentence2vec technology and semi-su-pervised algorithm, which introduced the sentence vector to calculate the text similarity and selected the sentence template. It used random sampling validation to verify the final result. The experimental results show that the method has higher precision and recall values than the traditional semi-supervised algorithm. [ABSTRACT FROM AUTHOR]

Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR 2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results. [ABSTRACT FROM AUTHOR]

The set of references that typically appear toward the end of journal articles is sometimes, though not always, a field in bibliographic (citation) databases. But even if references do not constitute such a field, they can be useful as a preprocessing step in the automated extraction of other bibliographic data from articles, as well as in computer-assisted indexing of articles. Automation in data extraction and indexing to minimize human labor is key to the affordable creation and maintenance of large bibliographic databases. Extracting the components of references, such as author names, article title, journal name, publication date and other entities, is therefore a valuable and sometimes necessary task. This paper describes a two-step process using statistical machine learning algorithms, to first locate the references in HTML medical articles and then to parse them. Reference locating identifies the reference section in an article and then decomposes it into individual references. We formulate this step as a two-class classification problem based on text and geometric features. An evaluation conducted on 500 articles drawn from 100 medical journals achieves near-perfect precision and recall rates for locating references. Reference parsing identifies the components of each reference. For this second step, we implement and compare two algorithms. One relies on sequence statistics and trains a Conditional Random Field. The other focuses on local feature statistics and trains a Support Vector Machine to classify each individual word, followed by a search algorithm that systematically corrects low confidence labels if the label sequence violates a set of predefined rules. The overall performance of these two reference-parsing algorithms is about the same: above 99% accuracy at the word level, and over 97% accuracy at the chunk level. [ABSTRACT FROM AUTHOR]

This paper is concerned with the automatic recognition of dialogue acts (DAs) in multiparty conversational speech. We present a joint generative model for DA recognition in which segmentation and classification of DAs are carried out in parallel. Our approach to DA recognition is based on a switching dynamic Bayesian network (DBN) architecture. This generative approach models a set of features," related to lexical content and prosody, and incorporates a weighted interpolated factored language model. The switching DBN coordinates the recognition process by integrating the component models. The factored language model, which is estimated from multiple conversational data corpora, is used in conjunction with additional task-specific language models. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. We have carried out experiments on the AMI corpus of multimodal meeting recordings, using both manually transcribed speech, and the output of an automatic speech recognizer, and using different configurations of the generative model. Our results indicate that the system performs well both on reference and fully automatic transcriptions. A further significant improvement in recognition accuracy is obtained by the application of the discriminative reranking approach based on conditional random fields. [ABSTRACT FROM AUTHOR]

This paper presents a conditional random field (CRF) approach to fuse contextual dependencies in functional magnetic resonance imaging (fMRI) data for the detection of brain activation. The interactions among both activation (activated/inactive) labels and observed data of brain voxels are unified in a probabilistic framework based on the CRF, where the interaction strength can be adaptively adjusted in terms of the data similarity of neighboring sites. Compared to earlier detection methods, including statistical parametric mapping and Markov random field, the proposed method avoids the suppression of high frequency information and relaxes the strong assumption of conditional independence of observed data. Experimental results show that the proposed approach effectively integrates contextual constraints within the detection process and robustly detects brain activities from fMRI data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to tackle the problems of the target boundary fuzz and the low accuracy of the top-down saliency target detection method,this paper proposed a top-down significance target detection method combining the conditional random field (CRF)and manifold ranking (MR). First of all.it super-pixel segmented the image and used the feature factor extracted from the super pixel block as the node to establish the undirected graph model. Then, it got saliency value of the node through the random field with the target prior and modified by the manifold ranking with the edge background prior. Finally, it expanded the initial saliency target to get the final saliency map. Experimental results show that the target boundary is clear in the target detection of pedestrians, cars and bicycles. Compared with the CRF-based method, this method has better robustness while ensuring the efficiency of the operation. [ABSTRACT FROM AUTHOR]

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the maturity of meteorological simulation technology, the research literature in this field is undergoing a rapid increase. The published literature can provide useful guidance for current research to get scientific results; however, it tends to be rather time consuming to obtain exact knowledge from massive literature, and it is necessary to transform the literature into structured knowledge to meet the efficient management, sharing, and reuse of meteorological simulation knowledge. In this paper, methods of meteorological simulation knowledge extraction and knowledge graph construction are proposed. A deep learning model based on bilateral long short-term memory-conditional random field (BiLSTM-CRF) is used to extract the meteorological simulation knowledge from the massive literature. Then, the Neo4j graph database is used to construct the meteorological simulation knowledge graph. Based on the meteorological simulation knowledge graph, it can realize the structured storage and integration of meteorological simulation knowledge, which can bridge the gap in the transformation of massive literature to sharable and reusable knowledge. Furthermore, the meteorological simulation knowledge graph can be used as an expert resource and contribute to sustainable guidance and optimization for meteorological simulation research. [ABSTRACT FROM AUTHOR]

To make full use of the contextual information object recognition and scene understanding, a multi-granular context conditional random field (MGCCRF) model is presented to combine context information in a variety of scales. It is efficiently implemented through extending the pairwise clique to the multi-granular context windows. In the fine-granular context window, the label consistency of similar features can be obtained with the probability of the label transferring between two adjacent pixels. At the same time, the spatial relationships among different classes in the coarse-granular context window are explicated in details. To train the MGCCRF model, a piecewise training method with the bound optimization algorithm is designed to improve the performance. Experiments on two real-world image databases show that compared with other methods, the modified conditional random field model is more competitive and effective in terms of the quantitative and qualitative labeling performance. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification. (1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents' regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy. [ABSTRACT FROM AUTHOR]

• The task of pedestrian retrieval is treated as a deep CRF labeling problem. • Group-wise similarity learning benefits the labeling results. • LSTM is good at modeling dependencies for combinatorial problems. • The end-to-end trainable architecture could fully utilizes the potential of deep CRF. The core of person re-identification (Re-ID) lies in robustly estimating similarities for each probe-gallery image pair. A common practice in existing works is to calculate the similarity of each image pair independently, ignoring relations between different probe-gallery pairs. In this paper, we present a deep learning conditional random field (Deep-CRF) graph to model group-wise similarities within a batch of images, and regard the Re-ID task as a CRF node labeling problem. Unlike the existing deep CRF based approach where the CRF inference is only involved in the training stage, our method intends to fully exploit the potential of CRF model, exhibiting inference consistency in both training and testing. Specifically, we design unary potentials for computing each probe-gallery similarity separately. To efficiently encode relationships between different probe-gallery pairs, pairwise potentials are built on an arbitrary node pair whose learning is achieved by a joint matching strategy using bidirectional LSTM. We pose the CRF inference as a RNN learning process, where unary and pairwise potentials are jointly optimized in an end-to-end manner. Extensive experiments on three large-scale person Re-ID datasets demonstrate the effectiveness of the proposed method. Our Deep-CRF achieves the best results compared with the previous graph-based deep learning approaches and substantially exceeds the existing deep CRF framework by 8% in Rank1 accuracy on CUHK03 dataset. It also behaves competitive among the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Since the result images obtained by deep semantic segmentation neural networks are usually not perfect, especially at object borders, the conditional random field (CRF) method is frequently utilized in the result post-processing stage to obtain the corrected classification result image. The CRF method has achieved many successes in the field of computer vision, but when it is applied to remote sensing images, overcorrection phenomena may occur. This paper proposes an end-to-end and localized post-processing method (ELP) to correct the result images of high-resolution remote sensing image classification methods. ELP has two advantages. (1) End-to-end evaluation: ELP can identify which locations of the result image are highly suspected of having errors without requiring samples. This characteristic allows ELP to be adapted to an end-to-end classification process. (2) Localization: Based on the suspect areas, ELP limits the CRF analysis and update area to a small range and controls the iteration termination condition. This characteristic avoids the overcorrections caused by the global processing of the CRF. In the experiments, ELP is used to correct the classification results obtained by various deep semantic segmentation neural networks. Compared with traditional methods, the proposed method more effectively corrects the classification result and improves classification accuracy. [ABSTRACT FROM AUTHOR]

Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice, and thus, it is necessary to establish a useful method to reverse the rockhead profile using site investigation results. As a general method to reflect the spatial distribution of geo-material properties based on field measurements, the conditional random field (CRF) was improved in this paper to simulate rockhead profiles. Besides, in geotechnical engineering practice, measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent. As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty, CRF was implemented with the aid of the Bayesian framework in this study. More importantly, this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work. The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result, the subjectivity in determining prior mean can be minimized. Finally, both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles, while the influence of the latter is less significant than that of the former.

In this paper, we propose a joint model of feature selection and ensemble learning for anaphora resolution in the resource-poor environment like the Indian languages. The proposed approach is based on multi-objective differential evolution (DE) that optimises five coreference resolution scorers, namely Muc , Bcub , Ceafm , Ceafe and Blanc . The main goal is to determine the best combination of different mention classifiers and the most relevant set of features for anaphora resolution. The proposed method is evaluated for three leading Indian languages, namely Hindi, Bengali and Tamil. Experiments on the benchmark datasets of ICON-2011 Shared Task on Anaphora Resolution in Indian Languages show that our proposed approach attains good level of accuracies, which are often better with respect to the state-of-the-art systems. It achieves the F-measure values of 71.89%, 59.61%, 52.55% 34.45% and 72.52% for Muc , Bcub , Ceafm , Ceafe and Blanc , respectively, for Bengali language. For Hindi we obtain the F-measure values of 33.27%, 63.06%, 49.59%, 49.06% and 55.45% for Muc , Bcub , Ceafm , Ceafe and Blanc metrics, respectively. In order to further show the efficacy of our proposed algorithm, we evaluate with Tamil, a language that belongs to a different family. This shows the F-measure values of 31.79%, 64.67%, 46.81%, 45.29% and 52.80% for Muc , Bcub , Ceafm , Ceafe and Blanc metrics, respectively. Experiments on Dutch show the F-measure values of 17.67%, 74.43%, 58.08%, 59.21% and 55.58% for Muc , Bcub , Ceafm , Ceafe and Blanc metrics, respectively. [ABSTRACT FROM AUTHOR]