Global contrast based salient region detection

Cheng, Ming–Ming; Zhang, Guoxin; Mitra, Niloy J.; Huang, Xiaolei; Hu, Shi‐Min

doi:10.1109/cvpr.2011.5995344

Cited by 2,176 publications

(2,400 citation statements)

References 33 publications

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“…Recent state-of-the-art saliency methods are using various features, like contrast [5] or texture [31]; others use less traditional ones, like analyzing the log spectrum of the image [12]. In [23] was shown that orientation information from the gradients in the vicinity of the interest points is a valuable feature for object representation: interest points are calculated as the local maxima of a modification of the Harris characteristic function [11], emphasizing both edges and corners in a balanced manner [14], then, based on orientation information, relevant edges can be emphasized for creating a feature map by fusing edges with other features.…”

Section: Textural-directional Feature Mapmentioning

confidence: 99%

Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion

Manno-Kovacs

Kovács

2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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We present a monocular obstacle avoidance method based on a novel image feature map built by fusing robust saliency features, to be used in embedded systems on lightweight autonomous vehicles. The fused salient features are a textural-directional Harris based feature map and a relative focus feature map. We present the generation of the fused salient map, along with its application for obstacle avoidance. Evaluations are performed from a saliency point of view, and for the assessment of the method's applicability for obstacle avoidance in simulated environments. The presented results support the usability of the method in embedded systems on lightweight unmanned vehicles.

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Section: Textural-directional Feature Mapmentioning

confidence: 99%

Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion

Manno-Kovacs

Kovács

2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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“…As argued by the pioneering perceptual research studies [26], [27], contrast is one of the influential factors in low-level visual saliency. Since the salient regions in the visual field would first pop out through different low-level features from their surroundings, numerous bottomup models [11]- [13], [28]- [30], [44] have been proposed to detect salient regions in images based on different mathematical principles. These saliency approaches built saliency models focusing on high contrast regions between candidate foreground objects and their surrounding backgrounds.…”

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%

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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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“…In most traditional methods, the salient objects were derived by the features extracted from pixels or regions, images were usually decomposed into several superpixel regions and final saliency maps consisted of these regions with their saliency scores [7][8][9][10]. The performance of these models rely on the segmentation methods and the selection of the feature.…”

Section: Introductionmentioning

confidence: 99%

Saliency Detection Via Dense Convolution Network

Fang¹,

Cao²,

Xing³

2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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Saliency detection is a fundamental problem in the field of image processing and computer vision. The convolutional model has been also used in saliency detection for its outstanding performance on image classification and localization task. In this paper, we propose a novel way to detect the salient object by modifying the Dense Convolution Network (DenseNet). We replace the fully-connected layer and the final pooling layer in DenseNet into a convolution layer and a deconvolution layer to fit the saliency detection task. And our network ends up with a squared Euclidean loss layer for saliency regression. Our network is end-to-end architecture which outputs saliency maps directly. Experimental results demonstrate that our approach is competitive in comparison with the state-of-the-art approaches.

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Global contrast based salient region detection

Cited by 2,176 publications

References 33 publications

Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion

Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion

Correspondence Driven Saliency Transfer

Saliency Detection Via Dense Convolution Network

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