Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging
LUO Huilan① WAN Chengtao① KONG Fansheng②
①(School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China) ②(College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China)
A new salient region detection algorithm is proposed via KL divergence between color probability distributions of super-pixels and merging multi-scale saliency maps. Firstly, multi-scale super-pixel segmentations of an input image are computed. In each segmentation scale, an undirected close-loop connected graph is constructed, in which nodes are the super-pixels and the adjacent regions are expanded reasonably relying on the total number of super-pixels. Then, all the color values in each super-pixel are clustered in terms of their discriminative power to get the statistical probability distribution of the cluster labels for each super-pixel. Next, the edges between all adjacent super-pixel pairs are weighted with the harmonic-mean of KL divergence of their probability distributions, and then the multi-scale saliency maps are calculated according to boundary connectivity and region contrast. The final saliency map is obtained by calculating and optimizing the mean map of all the saliency maps with different scales. Experimental results on some large benchmark datasets demonstrate that the proposed algorithm outperforms some state-of-the-art methods, and has higher precision and recall rates. The proposed algorithm can also produce smooth saliency maps.
罗会兰,万成涛,孔繁胜. 基于KL散度及多尺度融合的显著性区域检测算法[J]. 电子与信息学报, 2016, 38(7): 1594-1601.
LUO Huilan, WAN Chengtao, KONG Fansheng. Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging. JEIT, 2016, 38(7): 1594-1601.
ITTI L, KOCH C, and NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
[2]
YANG J and YANG M H. Top-down visual saliency via joint CRF and dictionary learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2012: 2296-2303.
[3]
TONG N, LU H, RUAN X, et al. Salient object detection via bootstrap learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015: 1884-1892.
[4]
JIANG H. Weakly supervised learning for salient object detection using background images[OL]. http://arxiv.org/ pdf/1501.07492.pdf , 2015.
[5]
ZHAO R, OUYANG W, LI H, et al. Saliency detection by multi-context deep learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015: 1265-1274.
[6]
YAN Q, XU L, SHI J, et al. Hierarchical saliency detection [C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013: 1155-1162.
[7]
ZHU W, LIANG S, WEI Y, et al. Saliency optimization from robust background detection[C]. IEEE International Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 2814-2821.
[8]
YANG C, ZHANG L, LU H, et al. Saliency detection via graph-based manifold ranking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3166-3173.
[9]
TONG N, LU H, ZHANG Y, et al. Salient object detection via global and local cues[J]. Pattern Recognition, 2015, 48(10): 3258-3267.
[10]
KIM J, HAN D, TAI Y W, et al. Salient region detection via high-dimensional color transform[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 883-890.
[11]
ACHANTA R, ESTRADA F, WILS P, et al. Salient region detection and segmentation[C]. International Conference on Computer Vision Systems, Heraklion, 2008: 66-75.
[12]
CHENG M M, ZHANG G X, MITRA N J, et al. Global contrast based salient region detection[C]. IEEE International Conference on Computer Vision and Pattern Recognition, Colorado Springs, 2011: 409-416.
[13]
PERAZZI F, KRAHENBUHL P, PRITCH Y, et al. Saliency filters: Contrast based filtering for salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 733-740.
[14]
HOU X and ZHANG L. Saliency detection: A spectral residual approach[C]. IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, 2007: 1-8.
[15]
ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency- tuned salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009: 1597-1604.
Jianyong and TANG Zhenmin. An improved graph-based manifold ranking for salient object detection[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2555-2563. doi: 10.11999/JEIT150619.
[17]
WEI Y, WEN F, ZHU W, et al. Geodesic saliency using background priors[C]. Proceedings of the 12th European Conference on Computer Vision, Firenze, Italy, 2012: 29-42.
JIANG Yuwen, TAN Leyi, and WANG Shoujue. Saliency detected model based on selective edges prior[J]. Journal of Electronics & Information Technology, 2015, 37(1): 130-136. doi: 10.11999/JEIT140119.
[19]
WANG J, LU H, LI X, et al. Saliency detection via background and foreground seed selection[J]. Neurocomputing, 2015, 152(C): 359-368.
[20]
ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.
[21]
KHAN R, VAN DE WEIJER J, KHAN F S, et al. Discriminative color descriptors[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2866-2873.
[22]
JOHNSON D B. Efficient algorithms for shortest paths in sparse networks[J]. Journal of the ACM (JACM), 1977, 24(1): 1-13.
[23]
OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 1979, 9(1): 62-66.
[24]
HE K, SUN J, and TANG X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
