Saliency Detection Based on Multi-Scale Superpixel and Dictionary Learning

Abstract:

In recent years, saliency detection has been gaining increasing attention since it could significantly boost many content-based multimedia applications. In this paper, we propose a visual saliency detection algorithm based on multi-scale superpixel and dictionary learning . Firstly, in each scale space, we extract the boundaries as the training samples to learn a dictionary through sparse coding and dictionary learning methods. Then, according to reconstruction error of each superpixel, the saliency map is generated for each scale of superpixel. Finally, some saliency maps from different scale spaces are fused together to generate the final saliency map. The experimental results show that the proposed algorithm can highlight the salient regions uniformly and performs better compared with the other five methods.

• Periodical: Applied Mechanics and Materials (Volumes 701-702)
• Pages: 348-351
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.701-702.348
• Online since: December 2014
• Authors: Gang Hou, He Xin Yan, Fan Zhang, Hui Rong Hou, Ming Zhang*
• Keywords: Dictionary Learning, Multi-Scale, Saliency Detection, Sparse Coding, Superpixel

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