Abstract:Aiming at the fault segmentation caused by color density clustering segmentation model, a dominant colors clustering image segmentation algorithm is proposed based on visual saliency. Firstly, according to the spatial color information and Mean-shift smoothing results, the global saliency and region saliency of the image are computed and fused as the constraints of spatial clustering. Then, kernel density estimation is employed to compute dominant colors of image as initial clusters and the salient features are taken as regulated factors for clustering segmentation. Finally, regions are merged for final segmentation. The experiments are implemented on the standard segmentation database and the proposed algorithm is compared with several algorithms. The experimental results show the higher precision of the proposed algorithm on region contours. The proposed algorithm makes good use of the salient feature of image, reduces the inconsistency of the clustering results, and improves the accuracy of pixel clustering and the robustness of the segmentation.
司马海峰,米爱中,王志衡,杜守恒. 显著特征融合的主颜色聚类分割算法*[J]. 模式识别与人工智能, 2016, 29(6): 492-503.
SIMA Haifeng, MI Aizhong, WANG Zhiheng, DU Shouheng. Clustering Segmentation of Dominant Colors Based on Salient Feature Fusion. , 2016, 29(6): 492-503.
[1] ZOU W B, LIU Z, KPALMA K, et al. Unsupervised Joint Salient Region Detection and Object Segmentation. IEEE Trans on Image Processing, 2015, 24(11): 3858-3873. [2] 许新征,丁世飞,史忠植,等.图像分割的新理论和新方法.电子学报, 2010, 38(2A): 76-82. (XU X Z, DING S F, SHI Z Z, et al. New Theories and Methods of Image Segmentation. Acta Electronica Sinica, 2010, 38(2A): 76-82.) [3] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-Tuned Salient Region Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami,USA: IEEE, 2009:1597-1604. [4] YANG C, ZHANG L H, LU H C, et al. Saliency Detection via Graph-Based Manifold Ranking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE, 2013: 3166-3173. [5] PERAZZI F, KRHENBHL P, PRITCH Y, et al. Saliency Filters: Contrast Based Filtering for Salient Region Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012: 733-740. [6] LEE J E, PARK R H. Segmentation with Saliency Map Using Color and Depth Images. IET Image Processing, 2014, 9(1): 62-70. [7] YANG J, YANG M H. Top-Down Visual Saliency via Joint CRF and Dictionary Learning // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012: 2296-2303. [8] ACHANTA R, SUSSTRUNK S. Saliency Detection for Content-Aware Image Resizing // Proc of the 16th IEEE International Conference on Image Processing. Cairo, Egypt: IEEE, 2009: 1005-1008. [9] FENG S H, XU D, YANG X. Attention-Driven Salient Edge(s) and Region(s) Extraction with Application to CBIR. Signal Processing, 2010, 90(1): 1-15. [10] HARE J S, LEWIS P H. Salient Regions for Query by Image Content // Proc of the 3rd International Conference on Image and Video Retrieval. Heidelberg, Germany: Springer-Verlag, 2004: 317-325. [11] ACHANTA R, ESTRADA F, WILS P, et al. Salient Region Detection and Segmentation // Proc of the 6th International Conference on Computer Vision Systems. Heidelberg, Germany: Springer-Verlag, 2008: 66-75. [12] JIANG H Z, WANG J D, YUAN Z J, et al. Salient Object Detection: A Discriminative Regional Feature Integration Approach // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE, 2013: 2083-2090. [13] 刘 毅,黄 兵,孙怀江,等.利用视觉显著性与图割的图像分割算法.计算机辅助设计与图形学学报, 2013, 25(3): 402-409. (LIU Y, HUANG B, SUN H J, et al. Image Segmentation Based on Visual Saliency and Graph Cuts. Journal of Computer Aided Design & Computer Graphics, 2013, 25(3): 402-409.) [14] LIU Z, LE MEUR O, LUO S H, et al. Saliency Detection Using Regional Histograms. Optics Letters, 2013, 38(5): 700-702. [15] HE K M, SUN J, TANG X O. Guided Image Filtering. IEEE Trans on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409. [16] 黄志勇,何发智,蔡贤涛,等.一种随机的视觉显著性检测算法.中国科学:信息科学, 2011, 41(7): 863-874. (HUANG Z Y, HE F Z, CAI X T, et al. Efficient Random Saliency Map Detection. Sciences China (Information Sciences) 2011, 41(7): 863-874.) [17] 胡正平,孟鹏权.全局孤立性和局部同质性图表示的随机游走显著目标检测算法.自动化学报, 2011, 37(10): 1279-1284. (HU Z P, MENG P Q. Graph Presentation Random Walk Salient Object Detection Algorithm Based on Global Isolation and Local Homogeneity. Acta Automatica Sinica, 2013, 37(10): 1279-1284.) [18] 钱鹏江,王士同,邓赵红.基于稀疏Parzen窗密度估计的快速自适应相似度聚类方法.自动化学报, 2011, 37(2): 179-187. (QIAN P J, WANG S T, DENG Z H. Fast Adaptive Similarity-Based Clustering Using Sparse Parzen Window Density Estimation. Acta Automatica Sinica, 2011, 37(2): 179-187.) [19] LI Y J, LU H M, ZHANG L F, et al. Color Image Segmentation Using Fast Density-Based Clustering Method // ZHANG Y, ed. Future Communication, Computing, Control and Management. Berlin, Germany: Springer-Verlag, 2012: 593-598. [20] 毛尚勤,黄心汉,王 敏.基于密度聚类的彩色图像分割方法.华中科技大学学报(自然科学版), 2011, 39(S2): 116-119. (MAO S Q, HUANG X H, WANG M. Color Image Segmentation Method Based on Density Clustering. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2011, 39(S2): 116-119.) [21] PELLEG D, MOORE A. X-means: Extending K-means with Efficient Estimation of the Number of Clusters // Proc of the 17th International Conference on Machine Learning. San Francisco, USA: Morgan Kaufmann, 2000: 727-734. [22] TAN K S, ISA N A M. Color Image Segmentation Using Histogram Thresholding-Fuzzy C-means Hybrid Approach. Pattern Recognition, 2011, 44(1): 1-15. [23] KUAN Y H, KUO C M, YANG N C. Color Based Image Salient Region Segmentation Using Novel Region Merging Strategy. IEEE Trans on Multimedia, 2008, 10(5): 832-845. [24] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 2004, 59(2): 167-181. [25] COMANICIU D, MEER P. Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619. [26] ESTRADA F J, JEPSON A D. Quantitative Evaluation of a Novel Image Segmentation Algorithm // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005, II: 1132-1139.