Abstract
In this paper, we propose a novel salient region detection approach. First, segment the original image into a set of superpixels to extract patch level features using low-level features in the patch. Next, global level features like element uniqueness and color contrast are created by previous patch level features. And then both patch level and global level features are gathered to a region to create region level features. Finally, all three level features are utilized to train support vector machines (SVM) classifier, and the trained SVM classifier is used to compute saliency map. The experiment results on the datasets show that the approach we propose performs outstanding in several state-of-the-art approaches.
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Duan, Q., Li, S., Mao, M. (2016). Salient Region Detection Using Multilevel Image Features. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-10-2338-5_23
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DOI: https://doi.org/10.1007/978-981-10-2338-5_23
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