Abstract
The exiting co-saliency detection methods achieve poor performance in computation speed and accuracy. Therefore, we propose a superpixel clustering based co-saliency detection method. The proposed method consists of three parts: multi-scale visual saliency map, weak co-saliency map and fusing stage. Multi-scale visual saliency map is generated by multi-scale superpixel pyramid with content-sensitive. Weak co-saliency map is computed by superpixel clustering feature space with RGB and CIELab color features as well as Gabor texture feature in order to the representation of global correlation. Lastly, a final strong co-saliency map is obtained by fusing the multi-scale visual saliency map and weak co-saliency map based on three kinds of metrics (contrast, position and repetition). The experiment results in the public datasets show that the proposed method improves the computation speed and the performance of co-saliency detection. A better and less time-consuming co-saliency map is obtained by comparing with other state-of-the-art co-saliency detection methods.
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Acknowledgment
This work was partially supported by National Natural Science Foundation of China (NSFC Grant Nos. 61170124, 61272258, 61301299, 61272005, 61572085), Provincial Natural Science Foundation of Jiangsu (Grant Nos. BK20151254, BK20151260), Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (Grant No. 93K172016K08), and Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Zhu, G., Ji, Y., Jiang, X., Xu, Z., Liu, C. (2017). Co-saliency Detection Based on Superpixel Clustering. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_24
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