Global contrast of superpixels based salient region detection

Authors:
Jie Wang;Caiming Zhang;Yuanfeng Zhou;Yu Wei;Yi Liu
Affiliations:
School of Computer Science and Technology, Shandong University, Jinan, China;School of Computer Science and Technology, Shandong University, Jinan, China,Shandong Provincial Key Laboratory of Digital Media Technology, Shandong University of Finance and Economics, Jinan, Ch ...;School of Computer Science and Technology, Shandong University, Jinan, China;School of Computer Science and Technology, Shandong University, Jinan, China;School of Computer Science and Technology, Shandong University, Jinan, China
Venue:
CVM'12 Proceedings of the First international conference on Computational Visual Media
Year:
2012

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Abstract

Reliable estimation of visual saliency has become an essential tool in image processing. In this paper, we propose a novel salient region detection algorithm, superpixel contrast (SC), consisting of three basic steps. First, we decompose a given image into compact, regular superpixels that abstract unnecessary details by a new superpixel algorithm, hexagonal simple linear iterative clustering (HSLIC). Then we define the saliency of each perceptually meaningful superpixel instead of rigid pixel grid, simultaneously evaluating global contrast differences and spatial coherence. Finally, we locate the key region and enhance its saliency by a focusing step. The proposed algorithm is simple to implement and computationally efficient. Our algorithm consistently outperformed all state-of-the-art detection methods, yielding higher precision and better recall rates, when evaluated on well-known publicly available data sets.