Abstract

This Letter presents a computational model for saliency detection in natural images. While existing approaches usually make use of low-level or high-level visual features for establishing the saliency models, our method relies on midlevel visual cues, i.e., the superpixel representation of the image. In the proposed approach, the given image is first partitioned into superpixels. A fully connected superpixel graph is then constructed, and the random walk on the graph is adopted to measure saliency. In addition, a scheme based on multiple segmentations is used for multiscale processing. Our model has the advantage of generating high-resolution saliency maps with well-defined object borders. Experimental results on publicly available datasets demonstrate the proposed model can outperform the compared state-of-the-art saliency models.

© 2012 Optical Society of America

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