A unified saliency detection framework for visible and infrared images

Zhang, Xufan; Wang, Yong; Yan, Jun; Chen, Zhenxing; Wang, Dianhong

doi:10.1007/s11042-020-08697-w

Cited by 2 publications

(2 citation statements)

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“…In the context based visual saliency detection algorithm, multiscale correction and context perception are combined so that the saliency of the target area in the saliency map is the highest, but the more it spreads to the edge, the lower the saliency, and at the edge, the image saliency is the most blurred [23,24].…”

Section: Context-based Visual Saliency Detection Algorithmmentioning

confidence: 99%

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An Image Saliency Detection Method Based on Combining Global and Local Information

Yang

Gong

Wang

2022

Mathematical Problems in Engineering

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In the field of computer vision, image saliency target detection can not only improve the accuracy of image detection but also accelerate the speed of image detection. In order to solve the existing problems of the saliency target detection algorithms at present, such as inconspicuous texture details and incomplete edge contour display, this paper proposes a saliency target detection algorithm integrating multiple information. The algorithm consists of three processes: preprocessing process, multi-information extraction process, and fusion optimization process. The frequency domain features of the image are calculated, the algorithm calculates the frequency domain features of the image, introduces power law transform and feature normalization, improves the frequency domain features of the image, saves the information of the target region, and inhibits the information of the background region. On three public MSRA, SED2, and ECSSD image datasets, the proposed algorithm is compared with other classical algorithms in subjective and objective comparison experiments. Experimental results show that the proposed algorithm can not only accurately and comprehensively extract significant target regions but also retain more texture information and complete edge information while satisfying the human visual experience. All evaluation indexes are significantly better than the comparison algorithm, showing good reliability and adaptability.

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Section: Context-based Visual Saliency Detection Algorithmmentioning

confidence: 99%

“…en, the color histogram of the input image is calculated, and only the high-frequency colors are retained. e remaining colors that appear less frequently are replaced by the nearest color in the histogram [23,28].…”

Section: Visual Saliency Detection Algorithm Based On Histogram Contrastmentioning

confidence: 99%