Saliency detection based on multi-instance images learning

Shouhong Wan; Peiquan Jin; Lihua Yue; Qian Huang

doi:10.1117/12.2197036

6 July 2015 Saliency detection based on multi-instance images learning

Shouhong Wan, Peiquan Jin, Lihua Yue, Qian Huang

Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96310O (2015) https://doi.org/10.1117/12.2197036
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

Abstract

Existing visual saliency detection methods are usually based on single image, however, without priori knowledge, the contents of single image are ambiguous, so visual saliency detection based on single image can’t extract region of interest. To solve it, we propose a novel saliency detection based on multi-instance images. Our method considers human’s visual psychological factors and measures visual saliency based on global contrast, local contrast and sparsity. It firstly uses multi-instance learning to get the center of clustering, and then computes feature relative dispersion. By fusing different weighted feature saliency map, the final synthesize saliency map is generated. Comparing with other saliency detection methods, our method increases the rate of hit.

Citation Download Citation

Shouhong Wan, Peiquan Jin, Lihua Yue, and Qian Huang "Saliency detection based on multi-instance images learning", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96310O (6 July 2015); https://doi.org/10.1117/12.2197036

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