Image salient region detection by fusing clustering and ranking
LIU Jie1,2,3, WANG Shengjin1,2,3
1. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
2. State Key Laboratory of Intelligent Technology and Systems, Beijing 100084, China;
3. Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China
Abstract：Salient region detection is an extremely challenging problem in computer vision. Most salient region detection algorithms determine the saliency of pixels in the image by directly computing the contrast between a pixel or a patch and its neighborhood within a certain range. When the image background is cluttered or the background and the salient objects in the image have some of the same image features, the detection capabilities of these traditional methods are decreased. A salient region detection framework based on re-clustering is used here. First, a clustering algorithm is used to group superpixels into a number of superpixel clusters by automatically computing the scale parameter and the number of clusters. The algorithm automatically selects possible background clusters from the superpixel clusters with the selected clusters used as queries in a ranking algorithm to obtain the final saliency map. Tests on two public salient region detection datasets show that the algorithm gives stable salient region detection results with better performance metrics than five other algorithms.
刘杰, 王生进. 融合聚类与排序的图像显著区域检测[J]. 清华大学学报（自然科学版）, 2016, 56(9): 913-919.
LIU Jie, WANG Shengjin. Image salient region detection by fusing clustering and ranking. Journal of Tsinghua University(Science and Technology), 2016, 56(9): 913-919.
[1] Papageorgiou C, Poggio T. A trainable system for object detection [J]. International Journal of Computer Vision, 2000, 38(1): 15-33.
[2] Mishra A K, Aloimonos Y, Cheong L F, et al. Active visual segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 639-653.
[3] ZHENG Liang, WANG Shengjin, LIU Z, et al. Fast image retrieval: Query pruning and early termination [J]. IEEE Transactions on Multimedia, 2015, 17(5): 648-659.
[4] LIU Jie, WANG Shengjin, Salient region detection via simple local and global contrast representation [J]. Neurocomputing, 2015, 147(1): 435-443.
[5] REN Zhixiang, GAO Shenghua, Chia L T, et al. Region-based saliency detection and its application in object recognition [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(5): 769-779.
[6] Brin S, Page L. The anatomy of a large-scale hypertextual Web search engine [J]. Computer Networks & Isdn Systems, 1998, 30(98): 107-117.
[7] YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking [C]//Proc of IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA: IEEE, 2013: 3166-3173.
[8] 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.
[9] Von Luxburg U. A tutorial on spectral clustering [J]. Statistics and computing, 2007, 17(4): 395-416.
[10] Van De Weijer J, Schmid C, Verbeek J, et al. Learning color names for real-world applications [J]. IEEE Transactions on Image Processing, 2009, 18(7): 1512-1523.
[11] ZHU Wangjiang, LIANG Shuang, WEI Yichen, et al. Saliency optimization from robust background detection [C]//Proc of IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE, 2014: 2814-2821.
[12] ZHOU Dengyong, Weston J, Gretton A, et al. Ranking on data manifolds [J]. Advances in Neural Information Processing Systems, 2004, 16(1): 169-176.
[13] ZHOU Dengyong, Bousquet O, Lal T N, et al. Learning with local and global consistency [J]. Advances in Neural Information Processing Systems, 2004, 16(16): 321-328.
[14] Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection [C]//Proc of IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE, 2009: 1597-1604.
[15] SHI Jianping, YAN Qiong, XU Li, et al. Hierarchical image saliency detection on extended CSSD [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(4): 717-729.
[16] CHENG Mingming, Mitra N J, HUANG Xiaolei, et al. Global contrast based salient region detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 569-582.
[17] Perazzi F, Krähenbühl P, Pritch Y, et al. Saliency filters: Contrast based filtering for salient region detection [C]//Proc of IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE, 2012: 733-740.
2016, Vol. 56 
