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Recent years have witnessed significant advances in deep learning based object detection. Despite being extensively explored, most existing detectors are designed to detect objects with relatively low-quality prediction of locations, i.e., they are often trained with the threshold of Intersection over Union (IoU) set as 0.5. This can yield low-quality or even noisy detections. Designing high quality object detectors which have a more precise localization (e.g. IoU > 0.5) remains an open challenge. In this paper, we propose a novel single-shot detection framework called Bidirectional Pyramid Networks (BPN) for high-quality object detection. It comprises two novel components: (i) Bidirectional Feature Pyramid structure and Anchor Refinement (AR). The bidirectional feature pyramid structure aims to use semantic-rich deep layer features to enhance the quality of the shallow layer features, and simultaneously use the spatially-rich shallow layer features to enhance the quality of deep layer features, leading to a stronger representation of both small and large objects for high quality detection. Our anchor refinement scheme gradually refines the quality of pre-designed anchors by learning multi-level regressors, giving more precise localization predictions. We performed extensive experiments on both PASCAL VOC and MSCOCO datasets, and achieved the best performance among all single-shot detectors. The performance was especially superior in the regime of high-quality detection.

Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations from large-scale training data, and propose a novel scheme of Sparse Online Learning of Image Similarity (SOLIS). In contrast to many existing image-similarity learning algorithms that are designed to work with low-dimensional data, SOLIS is able to learn image similarity from large-scale image data in sparse and high-dimensional spaces. Our encouraging results showed that the proposed new technique achieves highly competitive accuracy as compared to the state-of-the-art approaches but enjoys significant advantages in computational efficiency, model sparsity, and retrieval scalability, making it more practical for real-world multimedia retrieval applications.

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method., Comment: 12 pages

Aligning a given set of images is usually conducted in batch mode manner, which not only requires large amount of memory but also adjusts all the previous transformations to register an input image. To address this issue, we propose a novel approach to image alignment by incorporating the geometric transformation into online robust principal component analysis (PCA). Instead of calculating the warp update using noisy input samples like the conventional methods, we suggest directly linearizing the object function by performing warp update on the recovered samples, which corresponds to an efficient inverse composition algorithm. Since the basis matrix is kept constant for a given sample, both the latent vector and warp update can be very efficiently computed. Moreover, we present two basis updating methods for robust PCA, including the closed-form solution and stochastic gradient descent scheme. We have conducted the extensive experiments on the real-world tasks of background subtraction with camera motion and visual tracking on the challenging video sequences, whose promising results demonstrate the efficacy of our presented approach.

Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches, we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. To effectively extract the binary features from each patch surrounding the keypoint, we make use of treelets transform that can group the highly correlated data together and reduce the noise through the local analysis. Treelets is a multiresolution analysis tool, which provides an orthogonal basis to reflect the geometry of the noise-free data. To facilitate the real-world applications, we have proposed two novel approaches. One is the convolutional treelets that capture the image patch information locally and globally while reducing the computational cost. The other is the higher-order treelets that reflect the relationship between the rows and columns within image patch. An efficient sub-signature-based locality sensitive hashing scheme is employed for fast approximate nearest neighbor search in patch retrieval. Experimental evaluations on both synthetic data and the real-world Oxford dataset have shown that our proposed treelets binary feature retrieval methods outperform the state-of-the-art feature descriptors and classification-based approaches.

The conventional bag-of-visual-words (BoW) model is popular for the large-scale object retrieval system but suffers from the critical drawback of ignoring spatial information . RANSAC-based methods attempt to remedy this drawback, but often require traversing all the feature matches for each hypothesis , leading to the heavy computational cost which limits the number of gallery images to be verified for each online query. We propose an efficient direct spatial matching (DSM) approach to directly estimate the scale variation using region sizes, in which all feature matches voted for estimating geometric transformation . DSM is much faster than RANSAC-based methods and exhaustive enumeration approaches. A logarithmic term frequency- inverse document frequency (log tf-idf) weighting scheme is introduced to boost the performance of the base system. We have conducted extensive experimental evaluations on four benchmark datasets for object retrieval. The proposed DSM method, together with a carefully-tailored reranking scheme, achieves the state-of-the-art results on the Oxford buildings and Paris datasets, which demonstrates the efficacy and scalability of our novel DSM technique for large scale object retrieval systems.

In computer vision and multimedia analysis, it is common to use multiple features (or multimodal features) to represent an object. For example, to well characterize a natural scene image, we typically extract a set of visual features to represent its color, texture, and shape. However, it is challenging to integrate multimodal features optimally. Since they are usually high-order correlated, e.g., the histogram of gradient (HOG), bag of scale invariant feature transform descriptors, and wavelets are closely related because they collaboratively reflect the image texture. Nevertheless, the existing algorithms fail to capture the high-order correlation among multimodal features. To solve this problem, we present a new multimodal feature integration framework. Particularly, we first define a new measure to capture the high-order correlation among the multimodal features, which can be deemed as a direct extension of the previous binary correlation. Therefore, we construct a feature correlation hypergraph (FCH) to model the high-order relations among multimodal features. Finally, a clustering algorithm is performed on FCH to group the original multimodal features into a set of partitions. Moreover, a multiclass boosting strategy is developed to obtain a strong classifier by combining the weak classifiers learned from each partition. The experimental results on seven popular datasets show the effectiveness of our approach.

Image segmentation is an important research topic in image processing and computer vision. Recently, cosegmentation has received more and more attention. Although lots of research efforts have already studied this problem in the case of single object, there still lacks the deep investigation on multiple objects cosegmentation. In this paper, we try to attack this challenge by transferring the foreground segmentations using nonrigid mapping. We present a framework, in which we first take advantage of deformable part models to detect the foreground regions across the images, and the segmentation is formulated as an energy minimization problem on pixel labeling. We have conducted a set of experiments on the FlickrMFC dataset and iCoseg dataset. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods.

Auto face annotation, which aims to detect human faces from a facial image and assign them proper human names, is a fundamental research problem and beneficial to many real-world applications. In this work, we address this problem by investigating a retrieval-based annotation scheme of mining massive web facial images that are freely available over the Internet. In particular, given a facial image, we first retrieve the top $(n)$ similar instances from a large-scale web facial image database using content-based image retrieval techniques, and then use their labels for auto annotation. Such a scheme has two major challenges: 1) how to retrieve the similar facial images that truly match the query, and 2) how to exploit the noisy labels of the top similar facial images, which may be incorrect or incomplete due to the nature of web images. In this paper, we propose an effective Weak Label Regularized Local Coordinate Coding (WLRLCC) technique, which exploits the principle of local coordinate coding by learning sparse features, and employs the idea of graph-based weak label regularization to enhance the weak labels of the similar facial images. An efficient optimization algorithm is proposed to solve the WLRLCC problem. Moreover, an effective sparse reconstruction scheme is developed to perform the face annotation task. We conduct extensive empirical studies on several web facial image databases to evaluate the proposed WLRLCC algorithm from different aspects. The experimental results validate its efficacy. We share the two constructed databases "WDB" (714,454 images of 6,025 people) and "ADB" (126,070 images of 1,200 people) with the public. To further improve the efficiency and scalability, we also propose an offline approximation scheme (AWLRLCC) which generally maintains comparable results but significantly reduces the annotation time.

In this paper, we study the effective semi-supervised hashing method under the framework of regularized learning-based hashing. A nonlinear hash function is introduced to capture the underlying relationship among data points. Thus, the dimensionality of the matrix for computation is not only independent from the dimensionality of the original data space but also much smaller than the one using linear hash function. To effectively deal with the error accumulated during converting the real-value embeddings into the binary code after relaxation, we propose a semi-supervised nonlinear hashing algorithm using bootstrap sequential projection learning which effectively corrects the errors by taking into account of all the previous learned bits holistically without incurring the extra computational overhead. Experimental results on the six benchmark data sets demonstrate that the presented method outperforms the state-of-the-art hashing algorithms at a large margin.

Near-duplicate keyframe (NDK) retrieval techniques are critical to many real-world multimedia applications. Over the last few years, we have witnessed a surge of attention on studying near-duplicate image/keyframe retrieval in the multimedia community. To facilitate an effective approach to NDK retrieval on large-scale data, we suggest an effective Multi-Level Ranking (MLR) scheme that effectively retrieves NDKs in a coarse-to-fine manner. One key stage of the MLR ranking scheme is how to learn an effective ranking function with extremely small training examples in a near-duplicate detection task. To attack this challenge, we employ a semi-supervised learning method, semi-supervised support vector machines, which is able to significantly improve the retrieval performance by exploiting unlabeled data. Another key stage of the MLR scheme is to perform a fine matching among a subset of keyframe candidates retrieved from the previous coarse ranking stage. In contrast to previous approaches based on either simple heuristics or rigid matching models, we propose a novel Nonrigid Image Matching (NIM) approach to tackle near-duplicate keyframe retrieval from real-world video corpora in order to conduct an effective fine matching. Compared with the conventional methods, the proposed NIM approach can recover explicit mapping between two near-duplicate images with a few deformation parameters and find out the correct correspondences from noisy data simultaneously. To evaluate the effectiveness of our proposed approach, we performed extensive experiments on two benchmark testbeds extracted from the TRECVID2003 and TRECVID2004 corpora. The promising results indicate that our proposed method is more effective than other state-of-the-art approaches for near-duplicate keyframe retrieval.

Kernel methods have been widely used in pattern recognition. Many kernel classifiers such as Support Vector Machines (SVM) assume that data can be separated by a hyperplane in the kernel-induced feature space. These methods do not consider the data distribution and are difficult to output the probabilities or confidences for classification. This paper proposes a novel Kernel-based Maximum A Posteriori (KMAP) classification method, which makes a Gaussian distribution assumption instead of a linear separable assumption in the feature space. Robust methods are further proposed to estimate the probability densities, and the kernel trick is utilized to calculate our model. The model is theoretically and empirically important in the sense that: (1) it presents a more generalized classification model than other kernel-based algorithms, e.g., Kernel Fisher Discriminant Analysis (KFDA); (2) it can output probability or confidence for classification, therefore providing potential for reasoning under uncertainty; and (3) multi-way classification is as straightforward as binary classification in this model, because only probability calculation is involved and no one-against-one or one-against-others voting is needed. Moreover, we conduct an extensive experimental comparison with state-of-the-art classification methods, such as SVM and KFDA, on both eight UCI benchmark data sets and three face data sets. The results demonstrate that KMAP achieves very promising performance against other models.

Support vector machine (SVM) active learning is one popular and successful technique for relevance feedback in content-based image retrieval (CBIR). Despite the success, conventional SVM active learning has two main drawbacks. First, the performance of SVM is usually limited by the number of labeled examples. It often suffers a poor performance for the small-sized labeled examples, which is the case in relevance feedback. Second, conventional approaches do not take into account the redundancy among examples, and could select multiple examples that are similar (or even identical). In this work, we propose a novel scheme for explicitly addressing the drawbacks. It first learns a kernel function from a mixture of labeled and unlabeled data, and therefore alleviates the problem of small-sized training data. The kernel will then be used for a batch mode active learning method to identify the most informative and diverse examples via a min-max framework. Two novel algorithms are proposed to solve the related combinatorial optimization: the first approach approximates the problem into a quadratic program, and the second solves the combinatorial optimization approximately by a greedy algorithm that exploits the merits of submodular functions. Extensive experiments with image retrieval using both natural photo images and medical images show that the proposed algorithms are significantly more effective than the state-of-the-art approaches. A demo is available at http://msm.cais.ntu.edu.sg/LSCBIR/.

Face annotation in images and videos enjoys many potential applications in multimedia information retrieval. Face annotation usually requires many training data labeled by hand in order to build effective classifiers. This is particularly challenging when annotating faces on large-scale collections of media data, in which huge labeling efforts would be very expensive. As a result, traditional supervised face annotation methods often suffer from insufficient training data. To attack this challenge, in this paper, we propose a novel Transductive Kernel Fisher Discriminant (TKFD) scheme for face annotation, which outperforms traditional supervised annotation methods with few training data. The main idea of our approach is to solve the Fisher's discriminant using deformed kernels incorporating the information of both labeled and unlabeled data. To evaluate the effectiveness of our method, we have conducted extensive experiments on three types of multimedia testbeds: the FRGC benchmark face dataset, the Yahoo! web image collection, and the TRECVID video data collection. The experimental results show that our TKFD algorithm is more effective than traditional supervised approaches, especially when there are very few training data.

Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there is still a need to improve the overall tracking capability. In this paper, we presented a very appealing tracker based on the correlation filter framework. To tackle the problem of the fixed template size in kernel correlation filter tracker, we suggest an effective scale adaptive scheme. Moreover, the powerful features including HoG and color-naming are integrated together to further boost the overall tracking performance. The extensive empirical evaluations on the benchmark videos and VOT 2014 dataset demonstrate that the proposed tracker is very promising for the various challenging scenarios. Our method successfully tracked the targets in about 72% videos and outperformed the state-of-the-art trackers on the benchmark dataset with 51 sequences.

This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop effective optimization algorithms to solve the large-scale learning task efficiently. To further speed up the proposed scheme, we also propose a clustering-based approximation algorithm which can improve the scalability considerably. We have conducted an extensive set of empirical studies on a large-scale web facial image testbed, in which encouraging results showed that the proposed ULR algorithms can significantly boost the performance of the promising SBFA scheme. Accepted version

Document clustering plays an important role in large scale textual data analysis, which generally faces with great challenge of the high dimensional textual data. One remedy is to learn the high-level sparse representation by the sparse coding techniques. In contrast to traditional Gaussian noise-based sparse coding methods, in this paper, we employ a Poisson distribution model to represent the word-count frequency feature of a text for sparse coding. Moreover, a novel sparse-constrained Poisson regression algorithm is proposed to solve the induced optimization problem. Different from previous Poisson regression with the family of l1-regularization to enhance the sparse solution, we introduce a sparsity ratio measure which make use of both l1-norm and l2-norm on the learned weight. An important advantage of the sparsity ratio is that it bounded in the range of 0 and 1. This makes it easy to set for practical applications. To further make the algorithm trackable for the high dimensional textual data, a projected gradient descent algorithm is proposed to solve the regression problem. Extensive experiments have been conducted to show that our proposed approach can achieve effective representation for document clustering compared with state-of-the-art regression methods.

Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, this paper proposes a novel framework of Learning to Name Faces (L2NF) -- a unified multimodal learning approach for search-based face annotation, which consists of the following major components: (i) we enhance the weak labels of top-ranked similar images by exploiting the "label smoothness" assumption; (ii) we construct the multimodal representations of a facial image by extracting different types of features; (iii) we optimize the distance measure for each type of features using distance metric learning techniques; and finally (iv) we learn the optimal combination of multiple modalities for annotation through a learning to rank scheme. We conduct a set of extensive empirical studies on two real-world facial image databases, in which encouraging results show that the proposed algorithms significantly boost the naming accuracy of search-based face annotation task.

Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches [1,2], we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. A novel convolutional treelets approach is proposed to effectively extract the binary features from the patches. A corresponding sub-signature-based locality sensitive hashing scheme is employed for the fast approximate nearest neighbor search in patch retrieval. Experiments on both synthetic data and real-world images have shown that our method performs better than state-of-the-art descriptor-based and classification-based approaches.

Learning distance functions with side information plays a key role in many data mining applications. Conventional distance metric learning approaches often assume that the target distance function is represented in some form of Mahalanobis distance. These approaches usually work well when data are in low dimensionality, but often become computationally expensive or even infeasible when handling high-dimensional data. In this paper, we propose a novel scheme of learning nonlinear distance functions with side information. It aims to learn a Bregman distance function using a nonparametric approach that is similar to Support Vector Machines. We emphasize that the proposed scheme is more general than the conventional approach for distance metric learning, and is able to handle high-dimensional data efficiently. We verify the efficacy of the proposed distance learning method with extensive experiments on semi-supervised clustering. The comparison with state-of-the-art approaches for learning distance functions with side information reveals clear advantages of the proposed technique.

This paper investigates a retrieval-based annotation paradigm of mining web facial images for automated face annotation. In general, there are two key challenges for such an annotation paradigm. The first challenge is how to efficiently retrieve a short list of most similar facial images from facial image databases, and the second challenge is how to effectively perform annotation by exploiting these similar facial images and their weak labels which are often noisy and incomplete. In this paper, we mainly focus on tackling the second challenge of the retrieval-based face annotation paradigm. In particular, we propose an effective Weak Label Regularized Local Coordinate Coding (WLRLCC) technique, which exploits the local coordinate coding principle in learning sparse features, and at the same time employs the graph-based weak label regularization principle to enhance the weak labels of the short list of similar facial images. We present an efficient optimization algorithm to solve the WLRLCC task, and develop an effective sparse reconstruction scheme to perform the final face name annotation. We conduct a set of extensive empirical studies on a large-scale facial image database withatotal of 6,000 persons and over 600,000 web facial images, in which encouraging results show that the proposed WLRLCC algorithm significantly boosts the performance of the regular retrieval-based face annotation approaches.

We propose a novel approach to unsupervised facial image alignment. Differently from previous approaches, that are confined to affine transformations on either the entire face or separate patches, we extract a nonrigid mapping between facial images. Based on a regularized face model, we frame unsupervised face alignment into the Lucas-Kanade image registration approach. We propose a robust optimization scheme to handle appearance variations. The method is fully automatic and can cope with pose variations and expressions, all in an unsupervised manner. Experiments on a large set of images showed that the approach is effective.

Most state-of-the-art nonrigid shape recovery methods usually use explicit deformable mesh models to regularize surface deformation and constrain the search space. These triangulated mesh models heavily relying on the quadratic regularization term are difficult to accurately capture large deformations, such as severe bending. In this paper, we propose a novel Gaussian process regression approach to the nonrigid shape recovery problem, which does not require to involve a predefined triangulated mesh model. By taking advantage of our novel Gaussian process regression formulation together with a robust coarse-to-fine optimization scheme, the proposed method is fully automatic and is able to handle large deformations and outliers. We conducted a set of extensive experiments for performance evaluation in various environments. Encouraging experimental results show that our proposed approach is both effective and robust to nonrigid shape recovery with large deformations.

Near-duplicate image retrieval plays an important role in many real-world multimedia applications. Most previous approaches have some limitations. For example, conventional appearance-based methods may suffer from the illumination variations and occlusion issue, and local feature correspondence-based methods often do not consider local deformations and the spatial coherence between two point sets. In this paper, we propose a novel and effective Nonrigid Image Matching (NIM) approach to tackle the task of near-duplicate keyframe retrieval from real-world video corpora. In contrast to previous approaches, the NIM technique can recover an explicit mapping between two near-duplicate images with a few deformation parameters and find out the correct correspondences from noisy data effectively. To make our technique applicable to large-scale applications, we suggest an effective multi-level ranking scheme that filters out the irrelevant results in a coarse-to-fine manner. In our ranking scheme, to overcome the extremely small training size challenge, we employ a semi-supervised learning method for improving the performance using unlabeled data. To evaluate the effectiveness of our solution, we have conducted extensive experiments on two benchmark testbeds extracted from the TRECVID2003 and TRECVID2004 corpora. The promising results show that our proposed method is more effective than other state-of-the-art approaches for near-duplicate keyframe retrieval.

Kernel methods have been widely used in data classification. Many kernel-based classifiers like Kernel Support Vector Machines (KSVM) assume that data can be separated by a hyperplane in the feature space. These methods do not consider the data distribution. This paper proposes a novel Kernel Maximum A Posteriori (KMAP) classification method, which implements a Gaussian density distribution assumption in the feature space and can be regarded as a more generalized classification method than other kernel-based classifier such as Kernel Fisher Discriminant Analysis (KFDA). We also adopt robust methods for parameter estimation. In addition, the error bound analysis for KMAP indicates the effectiveness of the Gaussian density assumption in the feature space. Furthermore, KMAP achieves very promising results on eight UCI benchmark data sets against the competitive methods.

Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to each other. In this paper, we propose a novel scheme that exploits both semi-supervised kernel learning and batch mode active learning for relevance feedback in CBIR. In particular, a kernel function is first learned from a mixture of labeled and unlabeled examples. The kernel will then be used to effectively identify the informative and diverse examples for active learning via a min-max framework. An empirical study with relevance feedback of CBIR showed that the proposed scheme is significantly more effective than other state-of-the-art approaches.

Semi-supervised kernel learning is attracting increasing research interests recently. It works by learning an embedding of data from the input space to a Hilbert space using both labeled data and unlabeled data, and then searching for relations among the embedded data points. One of the most well-known semi-supervised kernel learning approaches is the spectral kernel learning methodology which usually tunes the spectral empirically or through optimizing some generalized performance measures. However, the kernel designing process does not involve the bias of a kernel-based learning algorithm, the deduced kernel matrix cannot necessarily facilitate a specific learning algorithm. To supplement the spectral kernel learning methods, this paper proposes a novel approach, which not only learns a kernel matrix by maximizing another generalized performance measure, the margin between two classes of data, but also leads directly to a convex optimization method for learning the margin parameters in support vector machines. Moreover, experimental results demonstrate that our proposed spectral kernel learning method achieves promising results against other spectral kernel learning methods.

Although AdaBoost has achieved great success, it still suffers from following problems: (1) the training process could be unmanageable when the number of features is extremely large; (2) the same weak classifier may be learned multiple times from a weak classifier pool, which does not provide additional information for updating the model; (3) there is an imbalance between the amount of the positive samples and that of the negative samples for multi-class classification problems. In this paper, we propose a two-stage AdaBoost learning framework to select and fuse the discriminative feature effectively. Moreover, an improved AdaBoost algorithm is developed to select weak classifiers. Instead of boosting in the original feature space, whose dimensionality is usually very high, multiple feature subspaces with lower dimensionality are generated. In the first stage, boosting is carried out in each subspace. Then the trained classifiers are further combined with simple fusion method in the second stage. Experimental results on facial expression recognition data demonstrate that our proposed algorithms not only reduce the computational cost for training, but also achieve comparable classification performance.

By using support vector machine (SVM), this paper presents a novel face retrieval scheme in face database based on lifting wavelets features. The relevance feedback mechanism is also performed. The scheme can be described in three stages as follows. First, lifting wavelets decomposition technique is employed because it not only can extract the optimal intrinsic features for representing a face image, but also can accelerate the speed of the wavelets transform. Second, Linear Discriminant Analysis (LDA) is adopted to reduce the feature dimensionality and enhance the class discriminability. Third, relevance feedback using SVM is applied to learn on user’s feedback to refine the retrieval performance. The experimental evaluation has been conducted on ORL dataset in which the results show that our proposed approach is effective and promising.

Face image retrieval in video sequences is a challenging task for many multimedia applications. This paper introduces a scheme based on lifting wavelets transform feature extraction for face image retrieval in video sequences. In our scheme, we first extract the key-frames rapidly from video sequences by a color feature for its effectiveness. Based on the extracted key-frames, we propose to employ the AdaBoost based face detection method and the lifting wavelets transform (LFWT) technique which provides good alternative of intensity representation for feature extraction of the detected faces. By combining the two stages above, we can perform the face image retrieval in video sequences task efficiently and effectively.

This paper proposes a new Enhanced Nearest Feature Space Classifier (ENFS) which inherits the generalization capability from Nearest Feature Space method. Additionally, estimated variance can optimize the class seperability in the sense of Bayes error, and has improve the classification power in reduced PCA subspace. Gabor wavelets representation of face images is an effective approach for both facial action recognition and face identification. Perform PCA dimensionality reduction on the downsampled Gabor Wavelets features can be effectively for face recognition. In our experiments, ENFS with proposed Gabor Wavelets Features shows very good performance, which can achieve 98.5% maximum correct recognition rate on ORL data set without any preprocessing step.