Saliency Detection via Graph-Based Manifold Ranking

Yang, Caiqin; Zhang, Lihe; Lu, Huchuan; Ruan, Xiang; Yang, Ming–Hsuan

doi:10.1109/cvpr.2013.407

Cited by 2,097 publications

(1,786 citation statements)

References 22 publications

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“…fold ranking (MR13) [15], saliency model via robust background detection (wCtr14) [18], saliency model via cellular automata (BSCA15) [21], and saliency model via bootstrap 2 . .…”

Section: Resultsmentioning

confidence: 99%

“…We bias the regions outside the salient window W with high probabilities for the background by (15) while give the regions inside the salient window W respectively conservative tendency for the foreground by (16). This is mainly due to the fact that our saliency estimation tends to be more 1 i accurate for the background than foreground.…”

Section: Rwr-based Final Saliency Derivationmentioning

confidence: 99%

“…For example, Jiang et al [14] proposed a graph-based method that models boundary regions as the absorbing nodes in a Markov chain and computes the saliency according to the absorption time in a random walk propagation. In [15], the saliency of image regions was measured by their relevance to the image boundary via a manifold ranking scheme. The work in [18] constructed a robust boundary prior based on boundary connectivity.…”

Section: B Bottom-up Saliency Detectionmentioning

confidence: 99%

“…We can ask how to identify correctly the salient region in complex scenario (a). The state-of-the-art methods, e.g., (b) the contrast prior based RC [11] and (c) the background prior based MR [15], face with ambiguity since they have no mechanism to incorporate additional contextual information. Our correspondence-based saliency transfer method (d) utilizes the saliency prior (f) from a set of support images (e) that share similar contextual scene information with the input image.…”

Section: Introductionmentioning

confidence: 99%

“…Top-down approaches [7]- [10] are goal-directed and usually adopt supervised learning with a specific class. Most of the saliency detection methods are based on bottomup visual attention mechanisms [11]- [15], [17], [18], [21], which are independent of the knowledge of the content in the image and utilize various low level features, such as intensity, color and orientation. Those bottom-up saliency models are generally based on different mathematical formulations of center-surround contrast or treat the image boundary as the background.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Correspondence Driven Saliency Transfer

Yang

Shen

Shao

et al. 2016

IEEE Trans. on Image Process.

138

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Abstract-In this paper, we show that large annotated data sets have great potential to provide strong priors for saliency estimation rather than merely serving for benchmark evaluations. To this end, we present a novel image saliency detection method called saliency transfer. Given an input image, we first retrieve a support set of best matches from the large database of saliency annotated images. Then, we assign the transitional saliency scores by warping the support set annotations onto the input image according to computed dense correspondences. To incorporate context, we employ two complementary correspondence strategies: a global matching scheme based on scene-level analysis and a local matching scheme based on patch-level inference. We then introduce two refinement measures to further refine the saliency maps and apply the random-walk-with-restart by exploring the global saliency structure to estimate the affinity between foreground and background assignments. Extensive experimental results on four publicly available benchmark data sets demonstrate that the proposed saliency algorithm consistently outperforms the current state-of-the-art methods.

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“…fold ranking (MR13) [15], saliency model via robust background detection (wCtr14) [18], saliency model via cellular automata (BSCA15) [21], and saliency model via bootstrap 2 . .…”

Section: Resultsmentioning

confidence: 99%

Section: Rwr-based Final Saliency Derivationmentioning

confidence: 99%

Section: B Bottom-up Saliency Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Correspondence Driven Saliency Transfer

Yang

Shen

Shao

et al. 2016

IEEE Trans. on Image Process.

138

View full text Add to dashboard Cite

show abstract

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

Ding

Gan

Bahrami

2022

Trans Emerging Tel Tech

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In recent years, understanding human behavior has become one of the most important topics in the field of computer vision research. The reason for this growing attention is the wide range of applications that can benefit from the results of this field of research. The human behavior analysis (HBA) includes a wide range of research areas from the detection of human motion and action.Datasets created through actions and detection of human activities make it possible to compare different detection methods with the same input data. Data mining and big data approaches are very popular for analyzing data related to human behavior and can be used to address the challenges of fast processing. This article provides a systematic survey of data mining and big data in the HBA. We focus on current datasets and models related to the detection of human behavior patterns in the literature. The purpose of this survey is to assist researchers in select appropriate datasets and models to evaluate algorithms, as well as to identify research gaps for future work. To achieve this goal, articles published between 2010 and 2021 have been reviewed. These articles fall into five general categories in terms of dataset focus: object detection, motion, action, activity, and behavior. This article provides a summary of data mining and big data models in the HBA, as well as related datasets based on these categories, to highlight promising research avenues for future work.

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An adaptive framework for saliency detection

Jia

Liu

Zhang

et al. 2019

Int J Imaging Syst Tech

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At present, people are inclined to use one saliency detection method to cover all the pixels in an image. However, every method has its own limitations. A single method may not yield a good performance at all image scenes. In this article, we propose a new adaptive framework to detect salient objects. For each pixel in an image, it adaptively selects an appropriate method according to the pixel context relationship. In our framework, an image is characterized by a set of binary maps, which are generated by randomly thresholding the image's initial saliency map. And then, we utilize the surroundedness cue, which are obtained by a series of operations on the binary maps, to classify all the pixels in an image. Furthermore, based on the classes, we choose methods to detect salient objects. Extensive experimental results on three benchmark datasets demonstrate that our method performs favorable against 11 state‐of‐the‐art methods.

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Saliency Detection via Graph-Based Manifold Ranking

Cited by 2,097 publications

References 22 publications

Correspondence Driven Saliency Transfer

Correspondence Driven Saliency Transfer

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

An adaptive framework for saliency detection

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