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.
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Wang, J., Zhang, C., Zhou, Y., Wei, Y., Liu, Y. (2012). Global Contrast of Superpixels Based Salient Region Detection. In: Hu, SM., Martin, R.R. (eds) Computational Visual Media. CVM 2012. Lecture Notes in Computer Science, vol 7633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34263-9_17
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DOI: https://doi.org/10.1007/978-3-642-34263-9_17
Publisher Name: Springer, Berlin, Heidelberg
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