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27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Yin Hou, Xiaodi Koch, Christof Rehg, James M. Yuille, Alan L. Georgia Tech Atlanta GA USA CALTECH Pasadena CA 91125 USA Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781479951178

In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient object benchmarks, called the dataset design bias, by over emphasising the stereotypical concepts of saliency. The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing. Based on our analysis, we propose a new high quality dataset that offers both fixation and salient object segmentation ground-truth. With fixations and salient object being presented simultaneously, we are able to bridge the gap between fixations and salient objects, and propose a novel method for salient object segmentation. Finally, we report significant benchmark progress on 3 existing datasets of segmenting salient objects.

关键词： Saliency dataset analysis eye fixation salient object segmentation

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Multi-scale Spatial-Temporal Feature Aggregating for Video salient object segmentation 4

Multi-scale Spatial-Temporal Feature Aggregating for Video S...

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4th IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Mu, Changhong Yuan, Zebin Ouyang, Xiuqin Wang, Bo Soochow Univ Golden Mantis Inst Architecture Suzhou Peoples R China

ISBN: (纸本)9781728136608

This paper proposes an algorithm based on supervised deep convolutional neural networks (CNNs), which fully extracts and fuses spatial-temporal information of frames to enhance the video saliency detection performance. Conventional video saliency detection methods that exist some problems (e.g. cumulative miscellaneous information, acquisition of spatial-temporal information separately, time-consuming algorithm, etc.), not only cannot make the spatial temporal information fully integrated, but cannot meet the real-time requirements. A Multi-scale Spatial Feature Extraction Module (MSFEM) based on deep learning is first designed for simultaneously extracting spatial features at multiple scales. Then, we further fuse the spatial-temporal features of the frames by adopting Multi-scale Spatial-Temporal Feature Refine Module (MSTFRM) to take full advantage of spatial-temporal information in order to achieve high-quality detections with strong spatial-temporal saliency consistency. After that, the network adopts end-to-end network training and testing to avoid unnecessary time overhead caused by pretreatment. To validate the method, we make comprehensive, quantitative evaluations between our method and 8 state-of-the-art techniques. All the results demonstrate our method's advantages in terms of accuracy and reliability.

关键词： video saliency salient object segmentation spatial temporal information deep learning

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Unsupervised salient object segmentation from color images

Unsupervised salient object segmentation from color images

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Conference on Visual Communications and Image Processing (VCIP)

作者： Liu, Zhi Wang, Lu Shen, Liquan Zhang, Zhaoyang Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200072 Peoples R China

ISBN: (纸本)9780819482341

This paper proposes an efficient approach for unsupervised segmentation of salient objects from color images. A set of Gaussian models are first estimated based on a pre-segmentation result of the input image, and then for each pixel, a set of normalized color likelihood measures to each Gaussian model are calculated. The color saliency and spatial saliency of Gaussian models are exploited to generate the pixel-wise saliency map. By thresholding the saliency map, the pixels are classified into object seed pixels, background seed pixels and uncertain pixels to obtain the trimap. For each pixel, the probability belonging to salient object/ background is evaluated using kernel density estimation, and the geodesic distances to salient object and background are calculated based on the object likelihood map. By comparing the two geodesic distances, uncertain pixels are finally classified into salient object or background. Experimental results demonstrate the better segmentation performance of the proposed approach.

关键词： salient object segmentation unsupervised segmentation saliency model saliency map

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A salient object segmentation framework using diffusion-based affinity learning

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114428-114428页

作者： Moradi, Morteza Bayat, Farhad Univ Zanjan Dept Elect Engn Zanjan Iran

In this paper, a salient object segmentation framework by using diffusion-based affinity learning and based on absorbing Markov chain (AMC) is proposed. Traditional approaches for structural modeling of images via local information and pairwise similarity graph by using, e.g. Gaussian heat kernel function, are insufficient for capturing the faithful relationships among the regions. According to the AMC principles, the more strong relationships result in lowering the time that a transient node becomes an absorbed one and consequently increases the transition probability between those nodes. To this end, a dense transition probability matrix is constructed based on an affinity matrix which learned using a diffusion process. Computing tensor product of the initial similarity graph with itself provides credible information about inter-relationships of nodes. Since conducting similarity propagation over such a tensor product graph imposes high computational costs, an iterative diffusion process is leveraged that introduces the same complexity as applying traditional diffusion processes on the original graph. As a fundamental benefit, such a process will enhance the accuracy and preciseness of saliency detection. Finally, as a complementary step, the saliency map will be refined by revisiting the saliency value of every single pixel. The experimental results on three major benchmark datasets demonstrate the efficiency of the proposed framework. More specifically, as expected, taking advantage of full learned affinity matrix can significantly improve the precision of the process.

关键词： salient object segmentation Absorbing Markov chain Affinity graph learning Diffusion process Tensor product graph

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Depth-Aware salient object Detection and segmentation via Multiscale Discriminative Saliency Fusion and Bootstrap Learning

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2017年第9期26卷 4204-4216页

作者： Song, Hangke Liu, Zhi Du, Huan Sun, Guangling Le Meur, Olivier Ren, Tongwei Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Minist Publ Secur Res Inst 3 Shanghai 201204 Peoples R China Univ Rennes 1 IRISA F-35042 Rennes France Nanjing Univ Software Inst Nanjing 210008 Jiangsu Peoples R China

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.

关键词： Depth information discriminative saliency fusion random forest saliency detection salient object segmentation

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salient object Detection and segmentation via Ultra-Contrast

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IEEE ACCESS 2018年 6卷 14870-14883页

作者： Tang, Liangzhi Meng, Fanman Wu, Qingbo Sowah, Nii Longdon Tan, Kai Li, Hongliang Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Sichuan Peoples R China

salient object detection aims at finding the most conspicuous objects in an image that highly catches the user's attention. The traditional contrast based salient object detection algorithms focus on highlighting the most dissimilar regions and generally fail to detect complex salient objects. In this paper, we propose a salient object detection principle from existing contrast based methods: dissimilarity produces contrast, while contrast leads to saliency. Guided by this principle, we propose a generalized framework to detect complex salient objects. First, we propose a set of region dissimilarity definitions inspired by diverse saliency cues. Then, multiple contrast contexts are encoded to derive dissimilarity matrices. Afterwards, multiple contrast transformations are designed to convert dissimilarity matrices into unified ultra-contrast features. Finally, these ultra-contrast features are mapped to saliency values through logistic regression. The proposed framework is capable of flexibly integrating different kinds of region dissimilarity definitions, region contexts, and contrast transformations. The experimental results demonstrate that our ultra-contrast based saliency detection method outperforms existing contrast based algorithms in terms of three metrics on four datasets.

关键词： Saliency detection salient object segmentation ultra-contrast region dissimilarity

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salient region detection and segmentation for general object recognition and image understanding

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Science China(Information Sciences) 2011年第12期54卷 2481-2490页

作者： HUANG TieJun 1 , TIAN YongHong 1 , LI Jia 2 & YU HaoNan 11 National Engineering Laboratory for Video Technology, School of Electrical Engineering and Computer Science, Peking University, Beijing 100871, China 2 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China National Engineering Laboratory for Video Technology School of Electrical Engineering and Computer Science Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

General object recognition and image understanding is recognized as a dramatic goal for computer vision and multimedia retrieval. In spite of the great efforts devoted in the last two decades, it still remains an open problem. In this paper, we propose a selective attention-driven model for general image understanding, named GORIUM (general object recognition and image understanding model). The key idea of our model is to discover recurring visual objects by selective attention modeling and pairwise local invariant features matching on a large image set in an unsupervised manner. Towards this end, it can be formulated as a four-layer bottom-up model, i.e., salient region detection, object segmentation, automatic object discovering and visual dictionary construction. By exploiting multi-task learning methods to model visual saliency simultaneously with the bottom-up and top-down factors, the lowest layer can effectively detect salient objects in an image. The second layer exploits a simple yet effective learning approach to generate two complementary maps from several raw saliency maps, which then can be utilized to segment the salient objects precisely from a complex scene. For the third layer, we have also implemented an unsupervised approach to automatically discover general objects from large image set by pairwise matching with local invariant features. Afterwards, visual dictionary construction can be implemented by using many state-of-the-art algorithms and tools available nowadays.

关键词： object recognition image understanding visual saliency salient object segmentation visual dictionary

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salient object detection via spectral matting

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PATTERN RECOGNITION 2016年 51卷 209-224页

作者： Naqvi, Syed S. Browne, Will N. Hollitt, Christopher Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

A number of pro-superpixel based saliency models have recently been proposed, which segment the image into small perceptually homogeneous regions before saliency computation. Such approaches ignore important object properties, resulting in inappropriate object annotations and considerably different saliency assignment to the various regions of an object. Although previous techniques employ multi-scale saliency maps in an attempt to rectify this problem, it becomes difficult to retain the characteristics of proto-objects after the first stage of processing. We introduce matting components based saliency to address the problems of inappropriate object annotations and inappropriate saliency assignment to object regions. The matting components account for proto-object properties by employing object aware spectral segmentation. To complement the matting component based saliency, we also employ the smallest eigenvectors of a matting Laplacian matrix. Color spatial distribution features are employed to capture global relationships at the pixel-level and assist the process of matting components based saliency computation. A novel joint optimization framework is introduced to fuse the features and learn important associated parameters. The contributions of the proposed approach are two-fold. The first contribution is the introduction of proto-objects aware spectral segmentation to obtain an accurate foreground saliency. The second contribution is the joint optimization of important parameters in conjunction with learning feature importance. In contrast to superpixel based approaches, the proposed model is able to completely annotate salient objects and assign similar saliency to various regions of the salient object. Moreover, the proposed approach shows robust and efficient performance across five challenging benchmark datasets when compared with 10 recently proposed state-of-the-art saliency detection models. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： salient object detection salient object segmentation Spectral clustering Image matting

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Focal Boundary Guided salient object Detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2019年第6期28卷 2813-2824页

作者： Wang, Yupei Zhao, Xin Hu, Xuecai Li, Yin Huang, Kaiqi Chinese Acad Sci Inst Automat Ctr Res Intelligent Syst & Engn Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Anhui Peoples R China Univ Wisconsin Dept Biostat & Med Informat Madison WI 53706 USA Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Chinese Acad Sci Inst Automat Ctr Res Intelligent Syst & Engn Natl Lab Pattern Recognit Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 20031 Peoples R China

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 lass 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.

关键词： Visual saliency detection salient object segmentation boundary detection deep learning

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Efficient saliency detection based on Gaussian models

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IET IMAGE PROCESSING 2011年第2期5卷 122-131页

作者： Liu, Z. Xue, Y. Yan, H. Zhang, Z. Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China Shanghai Univ Minist Educ Key Lab Adv Display & Syst Applicat Shanghai 200072 Peoples R China

This study presents an efficient saliency model mainly aiming at content-based applications such as salient object segmentation. The input colour image is first pre-segmented into a set of regions using the mean shift algorithm. A set of Gaussian models are estimated on the basis of segmented regions, and then for each pixel, a set of normalised colour likelihood measures to different Gaussian models are calculated. The colour saliency measure and spatial saliency measure of each Gaussian model are evaluated based on its colour distinctiveness and the spatial distribution, respectively. Finally, the pixel-wise colour saliency map and spatial saliency map are generated by summing the colour and spatial saliency measures of Gaussian models weighted by the normalised colour likelihood measures, and they are further combined to obtain the final saliency map. Experimental results on a dataset with 1000 images and ground truths demonstrate the better saliency detection performance of our saliency model.

关键词： saliency detection Gaussian model spatial saliency map image colour analysis colour saliency measure colour distinctiveness Computer vision and image processing techniques normalised colour likelihood measure spatial distribution colour image Gaussian processes Optical, image and video signal processing content-based application image segmentation spatial saliency measure salient object segmentation saliency model pixel-wise colour saliency map Other topics in statistics

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