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Unsupervised Joint Salient Region Detection and object segmentation

IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第11期24卷 3858-3873页

作者： Zou, Wenbin Liu, Zhi Kpalma, Kidiyo Ronsin, Joseph Zhao, Yong Komodakis, Nikos Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Univ Europeenne Bretagne Ctr Natl Rech Sci Inst Elect & Telecommun Rennes Inst Natl Sci Appliquees Rennes F-35708 Rennes France Peking Univ Shenzhen Grad Sch Key Lab Integrated Microsyst Sci & Engn Applicat Shenzhen 518055 Peoples R China Ecole Ponts ParisTech LIGM Lab F-77455 Marne La Vallee France

This paper presents a novel unsupervised algorithm to detect salient regions and to segment out foreground objects from background. In contrast to previous unidirectional saliency-based object segmentation methods, in which only the detected saliency map is used to guide the object segmentation, our algorithm mutually exploits detection/segmentation cues from each other. To achieve this goal, an initial saliency map is generated by the proposed segmentation driven low-rank matrix recovery model. Such a saliency map is exploited to initialize object segmentation model, which is formulated as energy minimization of Markov random field. Mutually, the quality of saliency map is further improved by the segmentation result, and serves as a new guidance for the object segmentation. The optimal saliency map and the final segmentation are achieved by jointly optimizing the defined objective functions. Extensive evaluations on MSRA-B and PASCAL-1500 datasets demonstrate that the proposed algorithm achieves the state-of-the-art performance for both the salient region detection and the object segmentation.

关键词： Saliency detection object segmentation low-rank matrix recovery

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Personal Fixations-Based object segmentation With object Localization and Boundary Preservation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 1461-1475页

作者： Li, Gongyang Liu, Zhi Shi, Ran Hu, Zheng Wei, Weijie Wu, Yong Huang, Mengke Ling, Haibin Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

As a natural way for human-computer interaction, fixation provides a promising solution for interactive image segmentation. In this paper, we focus on Personal Fixations-based object segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction. In particular, we first construct a new PFOS dataset by carefully collecting pixel-level binary annotation data over an existing fixation prediction dataset, such dataset is expected to greatly facilitate the study along the line. Then, considering characteristics of personal fixations, we propose a novel network based on object Localization and Boundary Preservation (OLBP) to segment the gazed objects. Specifically, the OLBP network utilizes an object Localization Module (OLM) to analyze personal fixations and locates the gazed objects based on the interpretation. Then, a Boundary Preservation Module (BPM) is designed to introduce additional boundary information to guard the completeness of the gazed objects. Moreover, OLBP is organized in the mixed bottom-up and top-down manner with multiple types of deep supervision. Extensive experiments on the constructed PFOS dataset show the superiority of the proposed OLBP network over 17 state-of-the-art methods, and demonstrate the effectiveness of the proposed OLM and BPM components. The constructed PFOS dataset and the proposed OLBP network are available at https://***/MathLee/OLBPNet4PFOS.

关键词： Image segmentation object segmentation Task analysis Visualization Visual systems Measurement Image edge detection Personal fixations interactive image segmentation object localization boundary preservation

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Improving object segmentation by using EEG signals and rapid serial visual presentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第22期74卷 10137-10159页

作者： Mohedano, Eva Healy, Graham McGuinness, Kevin Giro-i-Nieto, Xavier O'Connor, Noel E. Smeaton, Alan F. Dublin City Univ Insight Ctr Data Analyt Dublin 9 Ireland Univ Politecn Cataluna Image Proc Grp Catalunya Spain

This paper extends our previous work on the potential of EEG-based brain computer interfaces to segment salient objects in images. The proposed system analyzes the Event Related Potentials (ERP) generated by the rapid serial visual presentation of windows on the image. The detection of the P300 signal allows estimating a saliency map of the image, which is used to seed a semi-supervised object segmentation algorithm. Thanks to the new contributions presented in this work, the average Jaccard index was improved from 0.47 to 0.66 when processed in our publicly available dataset of images, object masks and captured EEG signals. This work also studies alternative architectures to the original one, the impact of object occupation in each image window, and a more robust evaluation based on statistical analysis and a weighted F-score.

关键词： Brain-computer interfaces Electroencephalography Rapid serial visual presentation object segmentation Interactive segmentation GrabCut algorithm

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Camouflaged object segmentation Based on MatchingRecognitionRefinement Network

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 15993-16007页

作者： Yan, Xinyu Sun, Meijun Han, Yahong Wang, Zheng Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Univ Tianjin Key Lab Machine Learning Tianjin 300350 Peoples R China Minist Educ Peoples Republ China Engn Res Ctr City Intelligence & Digital Governan Tianjin 300350 Peoples R China

In the biosphere, camouflaged objects take the advantage of visional wholeness by keeping the color and texture of the objects highly consistent with the background, thereby confusing the visual mechanism of other creatures and achieving a concealed effect. This is also the main reason why the task of camouflaged object detection is challenging. In this article, we break the visual wholeness and see through the camouflage from the perspective of matching the appropriate field of view. We propose a matching-recognition-refinement network (MRR-Net), which consists of two key modules, i.e., the visual field matching and recognition module (VFMRM) and the stepwise refinement module (SWRM). In the VFMRM, various feature receptive fields are used to match candidate areas of camouflaged objects of different sizes and shapes and adaptively activate and recognize the approximate area of the real camouflaged object. The SWRM then uses the features extracted by the backbone to gradually refine the camouflaged region obtained by VFMRM, thus yielding the complete camouflaged object. In addition, a more efficient deep supervision method is exploited, making the features from the backbone input into the SWRM more critical and not redundant. Extensive experimental results demonstrate that our MRR-Net runs in real-time (82.6 frames/s) and significantly outperforms 30 state-of-the-art models on three challenging datasets under three standard metrics. Furthermore, MRR-Net is applied to four downstream tasks of camouflaged object segmentation (COS), and the results validate its practical application value. Our code is publicly available at: https://***/XinyuYanTJU/MRR-Net.

关键词： Feature extraction Visualization Shape object recognition Task analysis object segmentation Image color analysis Camouflaged object segmentation (COS) deep learning polyp segmentation salient object detection

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Video object segmentation based on supervoxel for multimedia corpus construction

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NEUROCOMPUTING 2016年 215卷 128-137页

作者： Tian, Zhiqiang Lin, Yuping Emory Univ Sch Med Atlanta GA 30329 USA Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Foreign Studies Xian 710049 Shaanxi Peoples R China

Multimedia corpus is efficient for second language acquisition when we tag texts, scripts or objects in language materials and retrieve the corresponding video or audio parts. This paper presents an automatic video object segmentation method based on supervoxel in a multimedia corpus. In this method, a novel algorithm is proposed for generating the supervoxel. In 3D video volume, motion consistence of moving object is a frequent challenge for segmentation algorithm and a robust, long-term supervoxel could help in this situation. The segmentation process is considered as a minimization of an energy function where variables are labels of supervoxels. The supervoxel-based energy function consists of two terms, which are data term and smoothness term. The data term estimates the likelihood of a supervoxel to be labeled as moving object or background according to an appearance model and a motion model. The appearance model is based on an automatic processing for color collection. The motion model is derived from acting center-surround filter on an optical flow field. The supervoxel-based appearance and geometry cues are used to build smoothness term. The final segmentation can be obtained by minimizing the energy function which is defined on a spatio-temporal 3D video volume. We present experimental results of our method which outperforms the state-of-the-art methods on standard test sequences. (C) 2016 Elsevier B.V. All rights reserved.

关键词： object segmentation Supervoxel Energy function Graph cuts objectness score

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Persistent homology for object segmentation in multidimensional grayscale images

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PATTERN RECOGNITION LETTERS 2018年 112卷 277-284页

作者： Assaf, Rabih Goupil, Alban Vrabie, Valeriu Boudier, Thomas Kacim, Mohammad Univ Reims CReSTIC F-51100 Reims France UPMC Univ Paris 06 IPAL Sorbonne Univ UJFCNRSIMTNUS Singapore 138632 Singapore Holy Spirit Univ Kaslik Math Dept BP 446 Jounieh Lebanon

In this paper, we develop a methodology originating from algebraic topology, and we demonstrate its capability of performing multidimensional object segmentation without the need of prior parameters. Persistent homology is a method used in algebraic topology to study qualitative features of data that persist across varying scales. The construction of a topological complex on the image is followed by a filtration scheme that consists of composing a nested sequence of cell complexes on which the persistent homology is computed. The most persistent homology classes are extracted by identifying 1D and 2D chains with large lifespans, which allows salient objects in 2D and 3D images to be segmented and detected. A comparison between this method and other segmentation techniques on a synthetic image shows the advantages of the proposed method. The strength of this technique is reflected in its insensitivity to continuous deformations and perturbations of the input function and in its independence of prior parameters. The results obtained on real and biomedical 2D and 3D images also demonstrate the potential of this method. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Algebraic topology object segmentation Persistent homology

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PM-PM: PatchMatch With Potts Model for object segmentation and Stereo Matching

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第7期24卷 2182-2196页

作者： Xu, Shibiao Zhang, Feihu He, Xiaofei Shen, Xukun Zhang, Xiaopeng Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou 310027 Zhejiang Peoples R China

This paper presents a unified variational formulation for joint object segmentation and stereo matching, which takes both accuracy and efficiency into account. In our approach, depth-map consists of compact objects, each object is represented through three different aspects: 1) the perimeter in image space;2) the slanted object depth plane;and 3) the planar bias, which is to add an additional level of detail on top of each object plane in order to model depth variations within an object. Compared with traditional high quality solving methods in low level, we use a convex formulation of the multilabel Potts Model with PatchMatch stereo techniques to generate depth-map at each image in object level and show that accurate multiple view reconstruction can be achieved with our formulation by means of induced homography without discretization or staircasing artifacts. Our model is formulated as an energy minimization that is optimized via a fast primal-dual algorithm, which can handle several hundred object depth segments efficiently. Performance evaluations in the Middlebury benchmark data sets show that our method outperforms the traditional integer-valued disparity strategy as well as the original PatchMatch algorithm and its variants in subpixel accurate disparity estimation. The proposed algorithm is also evaluated and shown to produce consistently good results for various real-world data sets (KITTI benchmark data sets and multiview benchmark data sets).

关键词： object segmentation stereo matching Potts model PatchMatch multiple view reconstruction

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Joint Stereo Video Deblurring, Scene Flow Estimation and Moving object segmentation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 1748-1761页

作者： Pan, Liyuan Dai, Yuchao Liu, Miaomiao Porikli, Fatih Pan, Quan Australian Natl Univ Res Sch Engn Canberra ACT 2601 Australia Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Shaanxi Peoples R China Northwestern Polytech Univ Sch Automat Xian 710129 Shaanxi Peoples R China

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.

关键词： Estimation Cameras Motion segmentation object segmentation Image segmentation Kernel Semantics Stereo deblurring motion blur scene flow moving object segmentation joint optimization

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Accurate object segmentation for Video Sequences via Temporal-Spatial-Frequency Saliency Model

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IEEE INTELLIGENT SYSTEMS 2018年第1期33卷 18-28页

作者： Xu, Bing Niu, Yanxiong Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing Peoples R China Beihang Univ Beijing Peoples R China

In practical applications of video object segmentation, a major challenge is how to increase the accuracy and robustness of complex dynamic scenes and achieve positive scalability under camera displacement conditions. This paper proposes a novel approach to object segmentation by combining information in temporal, spatial, and frequency domains to realize complementary superiority. Four components-motion, color, luminance, and spectral residual from three domains-are applied to structure the temporal-spatial-frequency saliency (TSFS) model. A determining rule is defined to merge the components into a final model. The proposed model is evaluated on two representative video-sequence datasets. The experimental results indicate that the model is more accurate, robust, and effective than other state-of-the-art methods, and can satisfy the requirements of segmenting the object in complex dynamic scenes and large camera displacement conditions.

关键词： Image color analysis Cameras Frequency-domain analysis Video sequences Image segmentation object segmentation Visualization

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Neural network approach to background Modeling for video object segmentation

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2007年第6期18卷 1614-1627页

作者： Culibrk, Dubravko Marques, Oge Socek, Daniel Kalva, Hari Furht, Borko Florida Atlantic Univ Boca Raton FL 33431 USA

This paper presents a novel background modeling and subtraction approach for video object segmentation. A neural network (NN) architecture is proposed to form an unsupervised Bayesian classifier for this application domain. The constructed classifier efficiently handles the segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed NN serve as a model of the background and are temporally updated to reflect the observed statistics of background. The segmentation performance of the proposed NN is qualitatively and quantitatively examined and compared to two extant probabilistic object segmentation algorithms, based on a previously published test pool containing diverse surveillance-related sequences. The proposed algorithm is parallelized. on a subpixel level and designed to enable efficient hardware implementation.

关键词： automated surveillance background subtraction Neural Networks (NNs) object segmentation video processing.

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