Journal of System Simulation ›› 2016, Vol. 28 ›› Issue (9): 2138-2145.

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Improved Itti Visual Saliency Detection Based on Multi-scale Tensor Space

Wang Shimin, Ye Jihua, Cheng Bailiang, Wang Mingwen   

  1. College of Computer Information and Engineering, Jiangxi Normal University, Nanchang 330022, China
  • Received:2016-05-09 Revised:2016-07-11 Online:2016-09-08 Published:2020-08-14

Abstract: In view of internal dense uniform and clear borders image, through the saliency detection the target boundary is vague, so that the target object is not connected. In order to solve this problem, an improved Itti visual saliency detection method based on multi-scale tensor space was proposed. The method introduced the tensor space features, which preserved the original image spatial structure and correlation features, that could extract internal dense uniform image features, which made the target object connect, combining with saliency detection algorithm to finish feature extraction and target detection. Experimental results show that the proposed method can clearly and accurately extract saliency regions and achieve better detection results.

Key words: saliency detection, tensor space, multi-feature fusion, multi-scale transform

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