Incremental sparse saliency detection

Authors:
Yin Li;Yue Zhou;Lei Xu;Xiaochao Yang;Jie Yang
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

By the guidance of attention, human visual system is able to locate objects of interest in complex scene. We propose a new visual saliency detection model for both image and video. Inspired by biological vision, saliency is defined locally. Lossy compression is adopted, where the saliency of a location is measured by the Incremental Coding Length(ICL). The ICL is computed by presenting the center patch as the sparsest linear representation of its surroundings. The final saliency map is generated by accumulating the coding length. The model is tested on both images and videos. The results indicate a reliable and robust saliency of our method.