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One of the major challenges facing video object segmentation (VOS) is the gap between the training and test datasets due to unseen category in test set, as well as object appearance change over time in the video sequence. To overcome such challenges, an adaptive online framework for VOS is developed with bi-decoders mutual learning. We learn object representation per pixel with bi-level attention features in addition to CNN features, and then feed them into mutual learning bi-decoders whose outputs are further fused to obtain the final segmentation result. We design an adaptive online learning mechanism via a deviation correcting trigger such that bi-decoders online mutual learning will be activated when the previous frame is segmented well meanwhile the current frame is segmented relatively worse. Knowledge distillation from the well segmented previous frames, along with mutual learning between bi-decoders, improves generalization ability and robustness of VOS model. Thus, the proposed model adapts to the challenging scenarios including unseen categories, object deformation, and appearance variation during inference. We extensively evaluate our model on widely-used VOS benchmarks including DAVIS-2016, DAVIS-2017, YouTubeVOS-2018, YouTubeVOS-2019, and UVO. Experimental results demonstrate the superiority of the proposed model over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper focuses on the mask utilization of video object segmentation (VOS). The mask here mains the reference masks in the memory bank, i.e., several chosen high-quality predicted masks, which are usually used with the reference frames together. The reference masks depict the edge and contour features of the target object and indicate the boundary of the target against the background, while the reference frames contain the raw RGB information of the whole image. It is obvious that the reference masks could play a significant role in the VOS, but this is not well explored yet. To tackle this, we propose to investigate the mask advantages of both the encoder and the matcher. For the encoder, we provide a unified codebase to integrate and compare eight different mask-fused encoders. Half of them are inherited or summarized from existing methods, and the other half are devised by ourselves. We find the best configuration from our design and give valuable observations from the comparison. Then, we propose a new mask-enhanced matcher to reduce the background distraction and enhance the locality of the matching process. Combining the mask-fused encoder, mask-enhanced matcher and a standard decoder, we formulate a new architecture named MaskVOS, which sufficiently exploits the mask benefits for VOS. Qualitative and quantitative results demonstrate the effectiveness of our method. We hope our exploration could raise the attention of mask utilization in VOS. [ABSTRACT FROM AUTHOR]

Existing self-supervised methods pose one-shot video object segmentation (O-VOS) as pixel-level matching to enable segmentation mask propagation across frames. However, the two tasks are not fully equivalent since O-VOS is more reliant on semantic correspondence rather than accurate pixel matching. To remedy this issue, we explore a new self-supervised framework that integrates pixel-level correspondence learning with semantic-level adaptation. The pixel-level correspondence learning is performed through photometric reconstruction of adjacent RGB frames during offline training, while semantic-level adaption operates at test-time by enforcing a bi-directional agreement of the predicted segmentation masks. In addition, we further propose a new network architecture with multi-perspective feature mining mechanism which can not only enhance reliable features but also suppress noisy ones to facilitate more robust image matching. By training the network using the proposed self-supervised framework, we achieve state-of-the-art performance on widely adopted datasets, further closing up the gap between self-supervised learning methods and their fully supervised counterparts. [ABSTRACT FROM AUTHOR]

The Normalized Cut (NCut) model is a popular graph-based model for image segmentation. But it suffers from the excessive normalization problem and weakens the small object and twig segmentation. In this paper, we propose an Explored Normalized Cut (ENCut) model that establishes a balance graph model by adopting a meaningful-loop and a k-step random walk, which reduces the energy of small salient region, so as to enhance the small object segmentation. To improve the twig segmentation, our ENCut model is further enhanced by a new Random Walk Refining Term (RWRT) that adds local attention to our model with the help of an un-supervising random walk. Finally, a move-making based strategy is developed to efficiently solve the ENCut model with RWRT. Experiments on three standard datasets indicate that our model can achieve state-of-the-art results among the NCut-based segmentation models. [ABSTRACT FROM AUTHOR]

This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground truths. We also provide a benchmark suite for the task of camouflaged instance segmentation. In particular, we present an extensive evaluation of state-of-the-art instance segmentation methods on our newly constructed CAMO++ dataset in various scenarios. We also present a camouflage fusion learning (CFL) framework for camouflaged instance segmentation to further improve the performance of state-of-the-art methods. The dataset, model, evaluation suite, and benchmark will be made publicly available on our project page. [ABSTRACT FROM AUTHOR]

The task of video object segmentation is a fundamental but challenging problem in the field of computer vision. To deal with large variations in target objects and background clutter, we propose an online adaptive video object segmentation (VOS) framework, named Meta-VOS, that learns to adapt the target-specific segmentation. Meta-VOS builds an online adaptive learning process by exploiting cumulative expertise after searching for confidence patterns across different videos/frames, and then dynamically improves the model learning from two aspects: Meta-seg learner (i.e., module updating) and Meta-seg criterion (i.e., rule of expertise). As our goal is to rapidly determine which patterns best represent the essential characteristics of specific targets in a video, Meta-seg learner is introduced to adaptively learn to update the parameters and hyperparameters of segmentation network in very few gradient descent steps. Furthermore, a Meta-seg criterion of learned expertise, which is constructed to evaluate the Meta-seg learner for the online adaptation of the segmentation network, can confidently online update positive/negative patterns under the guidance of motion cues, object appearances and learned knowledge. Comprehensive evaluations on several benchmark datasets demonstrate the superiority of our proposed Meta-VOS when compared with other state-of-the-art methods applied to the VOS problem. [ABSTRACT FROM AUTHOR]

Recent advances in deep convolution neural networks (CNNs) boost the development of video salient object detection (SOD), and many remarkable deep-CNNs video SOD models have been proposed. However, many existing deep-CNNs video SOD models still suffer from coarse boundaries of the salient object, which may be attributed to the loss of high-frequency information. The traditional graph-based video SOD models can preserve object boundaries well by conducting superpixels/supervoxels segmentation in advance, but they perform weaker in highlighting the whole object than the latest deep-CNNs models, limited by heuristic graph clustering algorithms. To tackle this problem, we find a new way to address this issue under the framework of graph convolution networks (GCNs), taking advantage of graph model and deep neural network. Specifically, a superpixel-level spatiotemporal graph is first constructed among multiple frame-pairs by exploiting the motion cues implied in the frame-pairs. Then the graph data is imported into the devised multi-stream attention-aware GCN, where a novel Edge-Gated graph convolution (GC) operation is proposed to boost the saliency information aggregation on the graph data. A novel attention module is designed to encode the spatiotemporal sematic information via adaptive selection of graph nodes and fusion of the static-specific and the motion-specific graph embedding. Finally, a smoothness-aware regularization term is proposed to enhance the uniformity of salient object. Graph nodes (superpixels) inherently belonging to the same class will be ideally clustered together in the learned embedding space. Extensive experiments have been conducted on three widely used datasets. Compared with fourteen state-of-the-art video SOD models, our proposed method can well retain the salient object boundaries and possess a strong learning ability, which shows that this work is a good practice for designing GCNs for video SOD. [ABSTRACT FROM AUTHOR]

We present a learning model that makes full use of boundary information for salient object segmentation. Specifically, we come up with a novel loss function, i.e., Contour Loss, which leverages object contours to guide models to perceive salient object boundaries. Such a boundary-aware network can learn boundary-wise distinctions between salient objects and background, hence effectively facilitating the salient object segmentation. Yet the Contour Loss emphasizes the boundaries to capture the contextual details in the local range. We further propose the hierarchical global attention module (HGAM), which forces the model hierarchically to attend to global contexts, thus captures the global visual saliency. Comprehensive experiments on six benchmark datasets show that our method achieves superior performance over state-of-the-art ones. Moreover, our model has a real-time speed of 26 fps on a TITAN X GPU. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation, which aims at segmenting the whole tumor area, enhancing tumor core area, and tumor core area from each input multi-modality bio-imaging data, has received considerable attention from both academia and industry. However, the existing approaches usually treat this problem as a common semantic segmentation task without taking into account the underlying rules in clinical practice. In reality, physicians tend to discover different tumor areas by weighing different modality volume data. Also, they initially segment the most distinct tumor area, and then gradually search around to find the other two. We refer to the first property as the task-modality structure while the second property as the task-task structure, based on which we propose a novel task-structured brain tumor segmentation network (TSBTS net). Specifically, to explore the task-modality structure, we design a modality-aware feature embedding mechanism to infer the important weights of the modality data during network learning. To explore the task-task structure, we formulate the prediction of the different tumor areas as conditional dependency sub-tasks and encode such dependency in the network stream. Experiments on BraTS benchmarks show that the proposed method achieves superior performance in segmenting the desired brain tumor areas while requiring relatively lower computational costs, compared to other state-of-the-art methods and baseline models. [ABSTRACT FROM AUTHOR]

Salient object segmentation, edge detection, and skeleton extraction are three contrasting low-level pixel-wise vision problems, where existing works mostly focused on designing tailored methods for each individual task. However, it is inconvenient and inefficient to store a pre-trained model for each task and perform multiple different tasks in sequence. There are methods that solve specific related tasks jointly but require datasets with different types of annotations supported at the same time. In this article, we first show some similarities shared by these tasks and then demonstrate how they can be leveraged for developing a unified framework that can be trained end-to-end. In particular, we introduce a selective integration module that allows each task to dynamically choose features at different levels from the shared backbone based on its own characteristics. Furthermore, we design a task-adaptive attention module, aiming at intelligently allocating information for different tasks according to the image content priors. To evaluate the performance of our proposed network on these tasks, we conduct exhaustive experiments on multiple representative datasets. We will show that though these tasks are naturally quite different, our network can work well on all of them and even perform better than current single-purpose state-of-the-art methods. In addition, we also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel end-to-end learning neural network, i.e., MATNet, for zero-shot video object segmentation (ZVOS). Motivated by the human visual attention behavior, MATNet leverages motion cues as a bottom-up signal to guide the perception of object appearance. To achieve this, an asymmetric attention block, named Motion-Attentive Transition (MAT), is proposed within a two-stream encoder network to firstly identify moving regions and then attend appearance learning to capture the full extent of objects. Putting MATs in different convolutional layers, our encoder becomes deeply interleaved, allowing for close hierarchical interactions between object apperance and motion. Such a biologically-inspired design is proven to be superb to conventional two-stream structures, which treat motion and appearance independently in separate streams and often suffer severe overfitting to object appearance. Moreover, we introduce a bridge network to modulate multi-scale spatiotemporal features into more compact, discriminative and scale-sensitive representations, which are subsequently fed into a boundary-aware decoder network to produce accurate segmentation with crisp boundaries. We perform extensive quantitative and qualitative experiments on four challenging public benchmarks, i.e., DAVIS16, DAVIS17, FBMS and YouTube-Objects. Results show that our method achieves compelling performance against current state-of-the-art ZVOS methods. To further demonstrate the generalization ability of our spatiotemporal learning framework, we extend MATNet to another relevant task: dynamic visual attention prediction (DVAP). The experiments on two popular datasets (i.e., Hollywood-2 and UCF-Sports) further verify the superiority of our model (our code is available at https://github.com/tfzhou/MATNet). [ABSTRACT FROM AUTHOR]

Compressing an image with more bits automatically allocated to the region of interest (ROI) than to the background can both protect key information and reduce substantial redundancy. This paper models ROI image compression as an optimization problem of minimizing a weighted sum of the rate of the image and distortion of the ROI. The traditional framework solves this problem by cascading ROI prediction and ROI coding, through which achieving the optimized solution is impossible. To improve coding performance, we propose a novel deep-learning-based unified framework that can achieve rate distortion optimization for ROI compression. Specifically, the proposed framework includes a pair of ROI encoder and decoder convolutional neural networks and a learned entropy codec. The encoder network simultaneously generates multiscale representations that support efficient rate allocation and an implicit ROI mask that guides rate allocation. The proposed framework can automatically complete ROI image compression, and it can be optimized from data in an end-to-end manner. To effectively train the framework by back propagation, we develop a soft-to-hard ROI prediction scheme to make the entire framework differential. To improve visual quality, we propose a hierarchical distortion loss function to protect both pixel-level fidelity for ROI and structural similarity for the entire image. The proposed framework is implemented in two scenarios: salient-target and face-target ROI compression. Comparative experiments demonstrate the advantages of the proposed framework over the traditional framework, including considerably better subjective visual quality, significantly higher objective ROI compression performance and execution efficiency. [ABSTRACT FROM AUTHOR]

Stereo videos for the dynamic scenes often show unpleasant blurred effects due to the camera motion and the multiple moving objects with large depth variations. Given consecutive blurred stereo video frames, we aim to recover the latent clean images, estimate the 3D scene flow and segment the multiple moving objects. These three tasks have been previously addressed separately, which fail to exploit the internal connections among these tasks and cannot achieve optimality. In this paper, we propose to jointly solve these three tasks in a unified framework by exploiting their intrinsic connections. To this end, we represent the dynamic scenes with the piece-wise planar model, which exploits the local structure of the scene and expresses various dynamic scenes. Under our model, these three tasks are naturally connected and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes). By exploiting the blur model constraint, the moving objects and the 3D scene structure, we reach an energy minimization formulation for joint deblurring, scene flow and segmentation. We evaluate our approach extensively on both synthetic datasets and publicly available real datasets with fast-moving objects, camera motion, uncontrolled lighting conditions and shadows. Experimental results demonstrate that our method can achieve significant improvement in stereo video deblurring, scene flow estimation and moving object segmentation, over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recent state-of-the-art methods on focus region detection (FRD) rely on deep convolutional networks trained with costly pixel-level annotations. In this study, we propose a FRD method that achieves competitive accuracies but only uses easily obtained bounding box annotations. Box-level tags provide important cues of focus regions but lose the boundary delineation of the transition area. A recurrent constraint network (RCN) is introduced for this challenge. In our static training, RCN is jointly trained with a fully convolutional network (FCN) through box-level supervision. The RCN can generate a detailed focus map to locate the boundary of the transition area effectively. In our dynamic training, we iterate between fine-tuning FCN and RCN with the generated pixel-level tags and generate finer new pixel-level tags. To boost the performance further, a guided conditional random field is developed to improve the quality of the generated pixel-level tags. To promote further study of the weakly supervised FRD methods, we construct a new dataset called FocusBox, which consists of 5000 challenging images with bounding box-level labels. Experimental results on existing datasets demonstrate that our method not only yields comparable results than fully supervised counterparts but also achieves a faster speed. [ABSTRACT FROM AUTHOR]

Deep neural network-based semantic segmentation generally requires large-scale cost extensive annotations for training to obtain better performance. To avoid pixel-wise segmentation annotations that are needed for most methods, recently some researchers attempted to use object-level labels (e.g., bounding boxes) or image-level labels (e.g., image categories). In this paper, we propose a novel recursive coarse-to-fine semantic segmentation framework based on only image-level category labels. For each image, an initial coarse mask is first generated by a convolutional neural network-based unsupervised foreground segmentation model and then is enhanced by a graph model. The enhanced coarse mask is fed to a fully convolutional neural network to be recursively refined. Unlike the existing image-level label-based semantic segmentation methods, which require labeling of all categories for images that contain multiple types of objects, our framework only needs one label for each image and can handle images that contain multi-category objects. Only trained on ImageNet, our framework achieves comparable performance on the PASCAL VOC dataset with other image-level label-based state-of-the-art methods of semantic segmentation. Furthermore, our framework can be easily extended to foreground object segmentation task and achieves comparable performance with the state-of-the-art supervised methods on the Internet object dataset. [ABSTRACT FROM AUTHOR]

Unsupervised video object segmentation aims to automatically segment moving objects over an unconstrained video without any user annotation. So far, only few unsupervised online methods have been reported in the literature, and their performance is still far from satisfactory because the complementary information from future frames cannot be processed under online setting. To solve this challenging problem, in this paper, we propose a novel unsupervised online video object segmentation (UOVOS) framework by construing the motion property to mean moving in concurrence with a generic object for segmented regions. By incorporating the salient motion detection and the object proposal, a pixel-wise fusion strategy is developed to effectively remove detection noises, such as dynamic background and stationary objects. Furthermore, by leveraging the obtained segmentation from immediately preceding frames, a forward propagation algorithm is employed to deal with unreliable motion detection and object proposals. Experimental results on several benchmark datasets demonstrate the efficacy of the proposed method. Compared to state-of-the-art unsupervised online segmentation algorithms, the proposed method achieves an absolute gain of 6.2%. Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset. Our code is available on the project website: https://www.github.com/visiontao/uovos. [ABSTRACT FROM AUTHOR]

The performance of salient object segmentation has been significantly advanced by using the deep convolutional networks. However, these networks often produce blob-like saliency maps without accurate object boundaries. This is caused by the limited spatial resolution of their feature maps after multiple pooling operations and might hinder downstream applications that require precise object shapes. To address this issue, we propose a novel deep model—Focal Boundary Guided (Focal-BG) network. Our model is designed to jointly learn to segment salient object masks and detect salient object boundaries. Our key idea is that additional knowledge about object boundaries can help to precisely identify the shape of the object. Moreover, our model incorporates a refinement pathway to refine the mask prediction and makes use of the focal loss to facilitate the learning of the hard boundary pixels. To evaluate our model, we conduct extensive experiments. Our Focal-BG network consistently outperforms the state-of-the-art methods on five major benchmarks. We provide a detailed analysis of these results and demonstrate that our joint modeling of salient object boundary and mask helps to better capture the shape details, especially in the vicinity of object boundaries. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have shown an exciting promise in the urban street scene segmentation. However, many objects, such as poles and sign symbols, are relatively small, and they usually cannot be accurately segmented, since the larger objects usually contribute more to the segmentation loss. In this paper, we propose a new boundary-based metric that measures the level of spatial adjacency between each pair of object classes and find that this metric is robust against object size-induced biases. We develop a new method to enforce this metric into the segmentation loss. We propose a network, which starts with a segmentation network, followed by a new encoder to compute the proposed boundary-based metric, and then trains this network in an end-to-end fashion. In deployment, we only use the trained segmentation network, without the encoder, to segment new unseen images. Experimentally, we evaluate the proposed method using CamVid and CityScapes data sets and achieve a favorable overall performance improvement and a substantial improvement in segmenting small objects. [ABSTRACT FROM AUTHOR]

It is a challenging task to extract segmentation mask of a target from a single noisy video, which involves object discovery coupled with segmentation. To solve this challenge, we present a method to jointly discover and segment an object from a noisy video, where the target disappears intermittently throughout the video. Previous methods either only fulfill video object discovery, or video object segmentation presuming the existence of the object in each frame. We argue that jointly conducting the two tasks in a unified way will be beneficial. In other words, video object discovery and video object segmentation tasks can facilitate each other. To validate this hypothesis, we propose a principled probabilistic model, where two dynamic Markov networks are coupled–one for discovery and the other for segmentation. When conducting the Bayesian inference on this model using belief propagation, the bi-directional message passing reveals a clear collaboration between these two inference tasks. We validated our proposed method in five data sets. The first three video data sets, i.e., the SegTrack data set, the YouTube-objects data set, and the Davis data set, are not noisy, where all video frames contain the objects. The two noisy data sets, i.e., the XJTU-Stevens data set, and the Noisy-ViDiSeg data set, newly introduced in this paper, both have many frames that do not contain the objects. When compared with state of the art, it is shown that although our method produces inferior results on video data sets without noisy frames, we are able to obtain better results on video data sets with noisy frames. [ABSTRACT FROM AUTHOR]

To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images. In this paper, we present a simple yet effective approach for segmenting object proposals via a deep architecture of recursive neural networks (ReNNs), which hierarchically groups regions for detecting object candidates over scales. Unlike traditional methods that mainly adopt fixed similarity measures for merging regions or finding object proposals, our approach adaptively learns the region merging similarity and the objectness measure during the process of hierarchical region grouping. Specifically, guided by a structured loss, the ReNN model jointly optimizes the cross-region similarity metric with the region merging process as well as the objectness prediction. During inference of the object proposal generation, we introduce randomness into the greedy search to cope with the ambiguity of grouping regions. Extensive experiments on standard benchmarks, e.g., PASCAL VOC and ImageNet, suggest that our approach is capable of producing object proposals with high recall while well preserving the object boundaries and outperforms other existing methods in both accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Video object segmentation (VOS) is important for various computer vision problems, and handling it with minimal human supervision is highly desired for the large-scale applications. To bring down the supervision, existing approaches largely follow a data mining perspective by assuming the availability of multiple videos sharing the same object categories. It, however, would be problematic for the tasks that consume a single video. To address this problem, this paper proposes a novel approach that explores weakly labeled images to solve video object segmentation. Given a video labeled with a target category, images labeled with the same category are collected, from which noisy object exemplars are automatically discovered. After that the proposed approach extracts a set of region proposals on various frames and efficiently matches them with massive noisy exemplars in terms of appearance and spatial context. We then jointly select the best proposals across the video by solving a novel submodular problem that combines region voting and global region matching. Finally, the localization results are leveraged as strong supervision to guide pixel-level segmentation. Extensive experiments are conducted on two challenging public databases: Youtube-Objects and DAVIS. The results suggest that the proposed approach improves over previous weakly supervised/unsupervised approaches significantly, showing a performance even comparable with the several approaches supervised by the costly manual segmentations. [ABSTRACT FROM AUTHOR]

Given a set of images that contain objects from a common category, object co-segmentation aims at automatically discovering and segmenting such common objects from each image. During the past few years, object co-segmentation has received great attention in the computer vision community. However, the existing approaches are usually designed with misleading assumptions, unscalable priors, or subjective computational models, which do not have sufficient robustness for dealing with complex and unconstrained real-world image contents. This paper proposes a novel two-stage co-segmentation framework, mainly for addressing the robustness issue. In the proposed framework, we first introduce the concept of union background and use it to improve the robustness for suppressing the image backgrounds contained by the given image groups. Then, we also weaken the requirement for the strong prior knowledge by using the background prior instead. This can improve the robustness when scaling up for the unconstrained image contents. Based on the weak background prior, we propose a novel MR-SGS model, i.e., manifold ranking with the self-learned graph structure, which can infer suitable graph structures in a data-driven manner rather than building the fixed graph structure relying on the subjective design. Such capacity is critical for further improving the robustness in inferring the foreground/background probability of each image pixel. Comprehensive experiments and comparisons with other state-of-the-art approaches can demonstrate the effectiveness of the proposed work. [ABSTRACT FROM PUBLISHER]

Object skeletons are useful for object representation and object detection. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. But object skeleton extraction from natural images is very challenging, because it requires the extractor to be able to capture both local and non-local image context in order to determine the scale of each skeleton pixel. In this paper, we present a novel fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network. The network is trained by multi-task learning, where one task is skeleton localization to classify whether a pixel is a skeleton pixel or not, and the other is skeleton scale prediction to regress the scale of each skeleton pixel. Supervision is imposed at different stages by guiding the scale-associated side outputs toward the ground-truth skeletons at the appropriate scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to detect skeleton pixels using multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors. In addition, the usefulness of the obtained skeletons and scales (thickness) are verified on two object detection applications: foreground object segmentation and object proposal detection. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel depth-aware salient object detection and segmentation framework via multiscale discriminative saliency fusion (MDSF) and bootstrap learning for RGBD images (RGB color images with corresponding Depth maps) and stereoscopic images. By exploiting low-level feature contrasts, mid-level feature weighted factors and high-level location priors, various saliency measures on four classes of features are calculated based on multiscale region segmentation. A random forest regressor is learned to perform the discriminative saliency fusion (DSF) and generate the DSF saliency map at each scale, and DSF saliency maps across multiple scales are combined to produce the MDSF saliency map. Furthermore, we propose an effective bootstrap learning-based salient object segmentation method, which is bootstrapped with samples based on the MDSF saliency map and learns multiple kernel support vector machines. Experimental results on two large datasets show how various categories of features contribute to the saliency detection performance and demonstrate that the proposed framework achieves the better performance on both saliency detection and salient object segmentation. [ABSTRACT FROM AUTHOR]

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness. [ABSTRACT FROM PUBLISHER]

One of the most fundamental problems in image processing and computer vision is the inherent ambiguity that exists between texture edges and object boundaries in real-world images and video. Despite this ambiguity, many applications in computer vision and image processing often use image edge strength with the assumption that these edges approximate object depth boundaries. However, this assumption is often invalidated by real world data, and this discrepancy is a significant limitation in many of today's image processing methods. We address this issue by introducing a simple, low-level, and patch-consistency assumption that leverages the extra information present in video data to resolve this ambiguity. Through analyzing how well patches can be modeled by simple transformations over time, we can obtain an indication of which image edges correspond to texture edges versus object boundaries. Our approach is simple to implement and has the potential to improve a wide range of image and video-based applications by suppressing the detrimental effects of strong texture edges on regularization terms. We validate our approach by presenting results on a variety of scene types and directly incorporating our augmented edge map into existing image segmentation and optical flow applications, showing results that better correspond to object boundaries. [ABSTRACT FROM AUTHOR]

A dynamic texture (DT) is an extension of the texture to the temporal domain. How to segment a DT is a challenging problem. In this paper, we address the problem of segmenting a DT into disjoint regions. A DT might be different from its spatial mode (i.e., appearance) and/or temporal mode (i.e., motion field). To this end, we develop a framework based on the appearance and motion modes. For the appearance mode, we use a new local spatial texture descriptor to describe the spatial mode of the DT; for the motion mode, we use the optical flow and the local temporal texture descriptor to represent the temporal variations of the DT. In addition, for the optical flow, we use the histogram of oriented optical flow (HOOF) to organize them. To compute the distance between two HOOFs, we develop a simple effective and efficient distance measure based on Weber's law. Furthermore, we also address the problem of threshold selection by proposing a method for determining thresholds for the segmentation method by an offline supervised statistical learning. The experimental results show that our method provides very good segmentation results compared to the state-of-the-art methods in segmenting regions that differ in their dynamics. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel method based on a strategic combination of the active appearance model (AAM), live wire (LW), and graph cuts (GCs) for abdominal 3-D organ segmentation. The proposed method consists of three main parts: model building, object recognition, and delineation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the recognition part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW methods, resulting in the oriented AAM (OAAM). A multiobject strategy is utilized to help in object initialization. We employ a pseudo-3-D initialization strategy and segment the organs slice by slice via a multiobject OAAM method. For the object delineation part, a 3-D shape-constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC–OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT data set and also on the MICCAI 2007 Grand Challenge liver data set. The results show the following: 1) The overall segmentation accuracy of true positive volume fraction \TPVF > 94.3\% and false positive volume fraction \FPVF < 0.2\% can be achieved; 2) the initialization performance can be improved by combining the AAM and LW; 3) the multiobject strategy greatly facilitates initialization; 4) compared with the traditional 3-D AAM method, the pseudo-3-D OAAM method achieves comparable performance while running 12 times faster; and 5) the performance of the proposed method is comparable to state-of-the-art liver segmentation algorithm. The executable version of the 3-D shape-constrained GC method with a user interface can be downloaded from xlink:href="http://xinjianchen.wordpress.com/research/" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink"http://xinjianchen.wordpress.com/research/. [ABSTRACT FROM AUTHOR]

In this paper, a visual attention model is incorporated for efficient saliency detection, and the salient regions are employed as object seeds for our automatic object segmentation system. In contrast with existing interactive segmentation approaches that require considerable user interaction, the proposed method does not require it, i.e., the segmentation task is fulfilled in a fully automatic manner. First, we introduce a novel unified spectral-domain approach for saliency detection. Our visual attention model originates from a well-known property of the human visual system that the human visual perception is highly adaptive and sensitive to structural information in images rather than nonstructural information. Then, based on the saliency map, we propose an iterative self-adaptive segmentation framework for more accurate object segmentation. Extensive tests on a variety of cluttered natural images show that the proposed algorithm is an efficient indicator for characterizing the human perception and it can provide satisfying segmentation performance. [ABSTRACT FROM AUTHOR]

A new fully automatic object tracking and segmentation framework is proposed. The framework consists of a motion-based bootstrapping algorithm concurrent to a shape-based active contour. The shape-based active contour uses finite shape memory that is automatically and continuously built from both the bootstrap process and the active-contour object tracker. A scheme is proposed to ensure that the finite shape memory is continuously updated but forgets unnecessary information. Two new ways of automatically extracting shape information from image data given a region of interest are also proposed. Results demonstrate that the bootstrapping stage provides important motion and shape information to the object tracker. This information is found to be essential for good (fully automatic) initialization of the active contour. Further results also demonstrate convergence properties of the content of the finite shape memory and similar object tracking performance in comparison with an object tracker with unlimited shape memory. Tests with an active contour using a fixed-shape prior also demonstrate superior performance for the proposed bootstrapped finite-shape-memory framework and similar performance when compared with a recently proposed active contour that uses an alternative online learning model. [ABSTRACT FROM AUTHOR]

Processing images for specific targets on a large scale has to handle various kinds of contents with regular processing steps. To segment objects in one image, we utilized dual multiScalE Graylevel mOrphological open and close recoNstructions (SEGON) to build a background (BG) gray-level variation mesh, which can help to identify BG and object regions. It was developed from a macroscopic perspective on image BG gray levels and implemented using standard procedures, thus robustly dealing with large-scale database images. The image segmentation capability of existing methods can be exploited by the BG mesh to improve object segmentation accuracy. To evaluate the segmentation accuracy, the probability of coherent segmentation labeling, i.e., the normalized probability random index (PRI), between a computer-segmented image and the hand-labeled one is computed for comparisons. Content-based image retrieval (CBIR) was carried out to evaluate the object segmentation capability in dealing with large-scale database images. Retrieval precision–recall (PR) and rank performances, with and without SEGON, were compared. For multi-instance retrieval with shape feature, AdaBoost was used to select salient common feature elements. For color features, the histogram intersection between two scalable HSV descriptors was calculated, and the mean feature vector was used for multi-instance retrieval. The distance measure for color feature can be adapted when both positive and negative queries are provided. The normalized correlation coefficient of features among query samples was computed to integrate the similarity ranks of different features in order to perform multi-instance with multifeature query. Experiments showed that the proposed object segmentation method outperforms others by 21% in the PRI. Performing SEGON-enabled CBIR on large-scale databases also improves on the PR performance reported elsewhere by up to 42% at a recall rate of 0.5. The proposed object segmentation method can be extended to extract other image features, and new feature types can be incorporated into the algorithm to further improve the image retrieval performance. [ABSTRACT FROM PUBLISHER]

In this paper, we present a multiview approach to segment the foreground objects consisting of a group of people into individual human objects and track them across the video sequence. Depth and occlusion information recovered from multiple views of the scene is integrated into the object detection, segmentation, and tracking processes. Adaptive background penalty with occlusion reasoning is proposed to separate the foreground regions from the background in the initial frame. Multiple cues are employed to segment individual human objects from the group. To propagate the segmentation through video, each object region is independently tracked by motion compensation and uncertainty refinement, and the motion occlusion is tackled as layer transition. The experimental results implemented on both our sequences and other's sequence have demonstrated the algorithm's efficiency in terms of subjective performance. Objective comparison with a state-of-the-art algorithm validates the superior performance of our method quantitatively. [ABSTRACT FROM PUBLISHER]

This paper presents an extension of our recently introduced MAC model to deal with the initialization dependency problem that commonly appears in edge-based approaches. Its dynamic force field, unique bidirectionality, and constrained diffusion-based level set evolution provide great freedom in contour initialization and show significant improvements in initialization independency compared to other edge-based techniques. It handle more sophisticated topological changes than splitting and merging. It provides new potentials for edge-based active contour methods, particularly when detecting and localizing objects with unknown location, geometry, and topology. [ABSTRACT FROM AUTHOR]

This paper presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed ε-neighbor coherence segmentation criterion. More specifically, with an adaptive spatial scale and an appropriate intensity-difference scale, CCTA often achieves several sets of coherent neighboring pixels which maximize the probability of being a single image content (including kinds of complex backgrounds). In practice, each set of coherent neighboring pixels corresponds to a coherence class (CC). The fact that each CC just contains a single equivalence class (EC) ensures the separability of an arbitrary image theoretically. In addition, the resultant CCs are represented by tree-based data structures, named connected coherence tree (CCT)s. In this sense, CCTA is a graph-based image analysis algorithm, which expresses three advantages: 1) its fundamental idea, ε-neighbor coherence segmentation criterion, is easy to interpret and comprehend; 2) it is efficient due to a linear computational complexity in the number of image pixels; 3) both subjective comparisons and objective evaluation have shown that it is effective for the tasks of semantic object segmentation and figure-ground separation in a wide variety of images. Those images either contain tiny, long and thin objects or are severely degraded by noise, uneven lighting, occlusion, poor illumination, and shadow. [ABSTRACT FROM AUTHOR]

A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges-arch model-is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named L and H) and thereby classify the boundary. Second, the model is used to approximate the underlying noise- free material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the material boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise complicated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy. [ABSTRACT FROM AUTHOR]