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32
Saliency Detection via Graph-Based Manifold Ranking
"... Most existing bottom-up methods measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead of considering the contrast between the salient ..."
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Cited by 46 (3 self)
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Most existing bottom-up methods measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead of considering the contrast between the salient objects and their surrounding regions, we consider both foreground and background cues in a different way. We rank the similarity of the image elements (pixels or regions) with foreground cues or background cues via graph-based manifold ranking. The saliency of the image elements is defined based on their relevances to the given seeds or queries. We represent the image as a close-loop graph with superpixels as nodes. These nodes are ranked based on the similarity to background and foreground queries, based on affinity matrices. Saliency detection is carried out in a two-stage scheme to extract background regions and foreground salient objects efficiently. Experimental results on two large benchmark databases demonstrate the proposed method performs well when against the state-of-the-art methods in terms of accuracy and speed. We also create a more difficult benchmark database containing 5,172 images to test the proposed saliency model and make this database publicly available with this paper for further studies in the saliency field. 1.
Hierarchical Saliency Detection
"... When dealing with objects with complex structures, saliency detection confronts a critical problem – namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns. This issue is common in natural images and form ..."
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Cited by 33 (3 self)
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When dealing with objects with complex structures, saliency detection confronts a critical problem – namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns. This issue is common in natural images and forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze saliency cues. The final saliency map is produced in a hierarchical model. Different from varying patch sizes or downsizing images, our scale-based region handling is by finding saliency values optimally in a tree model. Our approach improves saliency detection on many images that cannot be handled well traditionally. A new dataset is also constructed. 1.
Salient object detection: A discriminative regional feature integration approach
- In CVPR
, 2013
"... Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. In this paper, we regard saliency map computation as a regression problem. Our method, which is based on multi-level image segmenta-tion, uses the supervised learni ..."
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Cited by 27 (4 self)
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Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. In this paper, we regard saliency map computation as a regression problem. Our method, which is based on multi-level image segmenta-tion, uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the saliency map. The contributions lie in two-fold. One is that we show our approach, which integrates the regional contrast, regional property and regional backgroundness descriptors together to form the master saliency map, is able to produce superior saliency maps to existing algorithms most of which combine saliency maps heuristically computed from different types of fea-tures. The other is that we introduce a new regional fea-ture vector, backgroundness, to characterize the back-ground, which can be regarded as a counterpart of the objectness descriptor [2]. The performance evaluation on several popular benchmark data sets validates that our approach outperforms existing state-of-the-arts. 1.
Saliency Detection via Dense and Sparse Reconstruction
"... In this paper, we propose a visual saliency detection al-gorithm from the perspective of reconstruction errors. The image boundaries are first extracted via superpixels as like-ly cues for background templates, from which dense and sparse appearance models are constructed. For each im-age region, we ..."
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Cited by 12 (1 self)
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In this paper, we propose a visual saliency detection al-gorithm from the perspective of reconstruction errors. The image boundaries are first extracted via superpixels as like-ly cues for background templates, from which dense and sparse appearance models are constructed. For each im-age region, we first compute dense and sparse reconstruc-tion errors. Second, the reconstruction errors are propa-gated based on the contexts obtained from K-means cluster-ing. Third, pixel-level saliency is computed by an integra-tion of multi-scale reconstruction errors and refined by an object-biased Gaussian model. We apply the Bayes formula to integrate saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against seventeen state-of-the-art methods in terms of precision and recall. In addition, the proposed algorithm is demonstrated to be more effective in highlighting salient objects uniformly and robust to background noise. 1.
Submodular Salient Region Detection
"... The problem of salient region detection is formulated as the well-studied facility location problem from operations research. High-level priors are combined with low-level features to detect salient regions. Salient region detection is achieved by maximizing a submodular objective function, which ma ..."
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Cited by 10 (1 self)
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The problem of salient region detection is formulated as the well-studied facility location problem from operations research. High-level priors are combined with low-level features to detect salient regions. Salient region detection is achieved by maximizing a submodular objective function, which maximizes the total similarities (i.e., total profits) between the hypothesized salient region centers (i.e., facility locations) and their region elements (i.e., clients), and penalizes the number of potential salient regions (i.e., the number of open facilities). The similarities are efficiently computed by finding a closed-form harmonic solution on the constructed graph for an input image. The saliency of a selected region is modeled in terms of appearance and spatial location. By exploiting the submodularity properties of the objective function, a highly efficient greedy-based optimization algorithm can be employed. This algorithm is guaranteed to be at least a (e − 1)/e ≈ 0.632-approximation to the optimum. Experimental results demonstrate that our approach outperforms several recently proposed saliency detection approaches. 1.
Pisa: Pixelwise image saliency by aggregating complementary appearance contrast measures with spatial priors
- In CVPR
"... Driven by recent vision and graphics applications such as image segmentation and object recognition, assigning pixel-accurate saliency values to uniformly highlight fore-ground objects becomes increasingly critical. More often, such fine-grained saliency detection is also desired to have a fast runt ..."
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Cited by 10 (2 self)
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Driven by recent vision and graphics applications such as image segmentation and object recognition, assigning pixel-accurate saliency values to uniformly highlight fore-ground objects becomes increasingly critical. More often, such fine-grained saliency detection is also desired to have a fast runtime. Motivated by these, we propose a generic and fast computational framework called PISA – Pixelwise Image Saliency Aggregating complementary saliency cues based on color and structure contrasts with spatial pri-ors holistically. Overcoming the limitations of previous methods often using homogeneous superpixel-based and color contrast-only treatment, our PISA approach directly performs saliency modeling for each individual pixel and makes use of densely overlapping, feature-adaptive obser-vations for saliency measure computation. We further im-pose a spatial prior term on each of the two contrast mea-sures, which constrains pixels rendered salient to be com-pact and also centered in image domain. By fusing com-plementary contrast measures in such a pixelwise adaptive manner, the detection effectiveness is significantly boosted. Without requiring reliable region segmentation or post-relaxation, PISA exploits an efficient edge-aware image rep-resentation and filtering technique and produces spatially coherent yet detail-preserving saliency maps. Extensive ex-periments on three public datasets demonstrate PISA’s su-perior detection accuracy and competitive runtime speed over the state-of-the-arts approaches. 1.
Salient region detection by UFO: Uniqueness, Focusness and Objectness
- In ICCV
"... The goal of saliency detection is to locate important pix-els or regions in an image which attract humans ’ visual at-tention the most. This is a fundamental task whose output may serve as the basis for further computer vision tasks like segmentation, resizing, tracking and so forth. In this paper w ..."
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Cited by 8 (2 self)
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The goal of saliency detection is to locate important pix-els or regions in an image which attract humans ’ visual at-tention the most. This is a fundamental task whose output may serve as the basis for further computer vision tasks like segmentation, resizing, tracking and so forth. In this paper we propose a novel salient region detec-tion algorithm by integrating three important visual cues namely uniqueness, focusness and objectness (UFO). In particular, uniqueness captures the appearance-derived vi-sual contrast; focusness reflects the fact that salient regions are often photographed in focus; and objectness helps keep completeness of detected salient regions. While uniqueness has been used for saliency detection for long, it is new to integrate focusness and objectness for this purpose. In fac-t, focusness and objectness both provide important salien-cy information complementary of uniqueness. In our ex-periments using public benchmark datasets, we show that, even with a simple pixel level combination of the three com-ponents, the proposed approach yields significant improve-ment compared with previously reported methods. 1.
Category-independent object-level saliency detection
- In ICCV
, 2013
"... It is known that purely low-level saliency cues such as frequency does not lead to a good salient object detection result, requiring high-level knowledge to be adopted for successful discovery of task-independent salient objects. In this paper, we propose an efficient way to combine such high-level ..."
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Cited by 7 (0 self)
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It is known that purely low-level saliency cues such as frequency does not lead to a good salient object detection result, requiring high-level knowledge to be adopted for successful discovery of task-independent salient objects. In this paper, we propose an efficient way to combine such high-level saliency priors and low-level appearance mod-els. We obtain the high-level saliency prior with the object-ness algorithm to find potential object candidates without the need of category information, and then enforce the con-sistency among the salient regions using a Gaussian MRF with the weights scaled by diverse density that emphasizes the influence of potential foreground pixels. Our model ob-tains saliency maps that assign high scores for the whole salient object, and achieves state-of-the-art performance on benchmark datasets covering various foreground statistics. 1.
How to Evaluate Foreground Maps?
"... The output of many algorithms in computer-vision is ei-ther non-binary maps or binary maps (e.g., salient object detection and object segmentation). Several measures have been suggested to evaluate the accuracy of these foreground maps. In this paper, we show that the most commonly-used measures for ..."
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Cited by 4 (0 self)
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The output of many algorithms in computer-vision is ei-ther non-binary maps or binary maps (e.g., salient object detection and object segmentation). Several measures have been suggested to evaluate the accuracy of these foreground maps. In this paper, we show that the most commonly-used measures for evaluating both non-binary maps and binary maps do not always provide a reliable evaluation. This includes the Area-Under-the-Curve measure, the Average-Precision measure, the Fβ-measure, and the evaluation measure of the PASCAL VOC segmentation challenge. We start by identifying three causes of inaccurate evaluation. We then propose a new measure that amends these flaws. An appealing property of our measure is being an intuitive generalization of the Fβ-measure. Finally we propose four meta-measures to compare the adequacy of evaluation mea-sures. We show via experiments that our novel measure is preferable. 1.
Saliency Detection on Light Field
"... Existing saliency detection approaches use images as in-puts and are sensitive to foreground/background similari-ties, complex background textures, and occlusions. We ex-plore the problem of using light fields as input for saliency detection. Our technique is enabled by the availability of commercia ..."
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Cited by 4 (0 self)
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Existing saliency detection approaches use images as in-puts and are sensitive to foreground/background similari-ties, complex background textures, and occlusions. We ex-plore the problem of using light fields as input for saliency detection. Our technique is enabled by the availability of commercial plenoptic cameras that capture the light field of a scene in a single shot. We show that the unique refocusing capability of light fields provides useful focusness, depths, and objectness cues. We further develop a new saliency de-tection algorithm tailored for light fields. To validate our approach, we acquire a light field database of a range of indoor and outdoor scenes and generate the ground truth saliency map. Experiments show that our saliency detection scheme can robustly handle challenging scenarios such as similar foreground and background, cluttered background, complex occlusions, etc., and achieve high accuracy and robustness. 1.
