Nonlocal center–surround reconstruction-based bottom-up saliency estimation

Chen, Xia; Qi, Fei; Shi, Guangming; Wang, Pengjin

doi:10.1016/j.patcog.2014.10.007

Cited by 26 publications

(12 citation statements)

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“…This method exploited features of color and luminance and was computationally efficient (we refer to this method as FT). The last selected saliency detection model for comparison was based on local sparse representation [15], which divided a test image into a number of patches at first; then, each patch was sparsely coded with its surrounding patches. Based on the learned dictionary, the sparse reconstruction error of each patch was computed as the saliency values for the corresponding patch (we refer to this method as LSR).…”

Section: Resultsmentioning

confidence: 99%

“…It has been verified that sparse representation is a useful tool for saliency detection, but it is difficult to obtain complete salient regions when applied to infrared images [15]. To solve this problem, here we propose a multiscale local sparse representation based approach to compute saliency for infrared images.…”

Section: Multiscale Local Sparse Representation Based Saliencymentioning

confidence: 99%

“…Given an original infrared image, we get two images with different scales based on the aforementioned multiscale idea and compute the saliency map using local sparse representation [15]. The detailed steps are given as follows.…”

Section: Multiscale Local Sparse Representation Based Saliencymentioning

confidence: 99%

“…Nowadays, researchers begin to apply sparse representation to image saliency detection. For example, in [15], Xia et al presented a saliency model based on a nonlocal reconstruction. In the method, the saliency was measured by the sparse reconstruction residual of representing the central patch with a linear combination of its surrounding patches sampled in a nonlocal manner.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Wang

Chen

Zhang

et al. 2017

Mathematical Problems in Engineering

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For infrared images, it is a formidable challenge to highlight salient regions completely and suppress the background noise effectively at the same time. To handle this problem, a novel saliency detection method based on multiscale local sparse representation and local contrast measure is proposed in this paper. The saliency detection problem is implemented in three stages. First, a multiscale local sparse representation based approach is designed for detecting saliency in infrared images. Using it, multiple saliency maps with various scales are obtained for an infrared image. These maps are then fused to generate a combined saliency map, which can highlight the salient region fully. Second, we adopt a local contrast measure based technique to process the infrared image. It divides the image into a number of image blocks. Then these blocks are utilized to calculate the local contrast to generate a local contrast measure based saliency map. In this map, the background noise can be suppressed effectually. Last, to make full use of the advantages of the above two saliency maps, we propose combining them together using an adaptive fusion scheme. Experimental results show that our method achieves better performance than several state-of-the-art algorithms for saliency detection in infrared images.

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Section: Resultsmentioning

confidence: 99%

Section: Multiscale Local Sparse Representation Based Saliencymentioning

confidence: 99%

Section: Multiscale Local Sparse Representation Based Saliencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Wang

Chen

Zhang

et al. 2017

Mathematical Problems in Engineering

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“…Theories related to human vision system can be helpful in CBIR by representing images in a form that is closer to human interpretation. For instance, visual saliency models can be incorporated in content representation frameworks for identifying perceptually significant regions, grabbing human attention [29]. This approach looks interesting and intuitive due to the fact that humans tend to look for specific objects in images while searching through the large databases.…”

Section: Introductionmentioning

confidence: 99%

Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

Ahmad

Sajjad

Mehmood

et al. 2015

J Real-Time Image Proc

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Item Type Article Authors Ahmad, J.; Sajjad, M.; Mehmood, Irfan; Rho, S.; Baik, S.W. Citation Ahmad J, Sajjad M, Mehmood I et al (2017) Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems. Journal of Real-Time Image Processing. 13(3): 431-447. Rights © Springer-Verlag Berlin Heidelberg 2015. Reproduced in accordance with the publisher's self-archiving policy. The final publication is available at Springer via https://doi. AbstractThe exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.

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Particle Swarm Optimization Based Salient Object Detection for Low Contrast Images

Zhang

et al. 2017

Neural Information Processing

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Nonlocal center–surround reconstruction-based bottom-up saliency estimation

Cited by 26 publications

References 50 publications

Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

Particle Swarm Optimization Based Salient Object Detection for Low Contrast Images

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