Abstract：Based on mean-standard deviation curve descriptor (MSCD),the intensity order mean-standard deviation descriptor (IOMSD) is proposed by introducing the idea of intensity order partition. Different from the fixed-position partitioning used in the construction of traditional descriptors,the sub-regions are partitioned according to the relationship of intensities among sample points. IOMSD overcomes the boundary errors of image deformation,and it is invariant to changes of linear illumination and monotonic intensity. Experimental results show that IOMSD has robust performance under image rotation,viewpoint change and illumination change. Moreover,it is stabler in image deformation than MSCD.
[1] Lowe D G. Distinctive Image Features from Scale-Invariant Key-Points. International Journal of Computer Vision,2004,60(2): 91-110 [2] Mikolajczyk K,Schmid C. A Performance Evaluation of Local Descriptors. IEEE Trans on Pattern Analysis and Machine Intelligence,2005,27(10): 1615-1630 [3] Zitova B,Flusser J. Image Registration Methods: A Survey. Image and Vision Computing,2003,21(11): 977-1000 [4] Qiu Guoping,Morris J,Fan Xunli. Visual Guided Navigation for Image Retrieval. Pattern Recognition,2007,40(6): 1711-1721 [5] Heikkila M,Pietikainen M,Schmid C. Description of Interest Regions with Local Binary Patterns. Pattern Recognition,2009,42(3): 425-436 [6] Ojala T,Pietikainen M,Harwood D. A Comparative Study of Texture Measures with Classification Based on Feature Distribution. Pattern Recognition,1996,29(1): 51-59 [7] Gupta R,Patil H,Mittal A. Robust Order-Based Methods for Feature Description // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco,USA,2010: 334-341 [8] Li S Z. Invariant Representation,Matching and Pose Estimations of 3D Space Curves under Similarity Transformations. Pattern Recognition,1997,30(3): 447-458 [9] Folkers A,Samet H. Content-Based Image Retrieval Using Fourier Descriptors on a Logo Database // Proc of the 16th International Conference on Pattern Recognition. Quebec,Canada,2002,3: 521-524 [10] Ardovini A,Cinque L,Sangineto E. Identifying Elephant Photos by Multi-Curve Matching. Pattern Recognition,2008,41(6): 1867-1877 [11] Zhao Xunpo,Hu Zhanyi. A Practical Rough Image Curve Matching Method Based on Accumulation of Evidence. Chinese Journal of Computers,2005,28(3): 357-367(in Chinese) (赵训坡,胡占义.一种实用的基于证据积累的图像曲线粗匹配方法.计算机学报,2005,28(3): 357-367) [12] Schmid C,Zisserman A. The Geometry and Matching of Lines and Curves over Multiple Views. International Journal of Computer Vision,2000,40(3): 199-233 [13] Lourakis M I A,Halkidis S T,Orphanoudakis S C. Matching Disparate Views of Planar Surfaces Using Projective Invariants. Image Vision Computing,2000,18(9): 673-683 [14] Fan Bin,Wu Fuchao,Hu Zhanyi. Line Matching Leveraged by Point Correspondences // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. San Francisco,USA,2010: 390-397 [15] Bay H,Ferraris V,van Gool L. Wide-Baseline Stereo Matching with Line Segments // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. San Diego,USA,2005: 329-336 [16] Mikolajczyk K,Schmid C,Zisserman A. Shape Recognition with Edge-Based Features // Harvey R,Bangham A,eds. Proc of the British Machine Vision Conference. Norwich,UK: BMVA Press,2003,Ⅱ: 779-788 [17] Wang Zhiheng,Wu Fuchao,Hu Zhanyi. MSLD: A Robust Descriptor for Line Matching. Pattern Recognition,2009,42(5): 941-953 [18] Wang Xuguang,Wang Zhiheng,Wu Fuchao. Harris Correlation and Feature Matching. Pattern Recognition and Artificial Intelligence,2009,22(4): 505-513(in Chinese) (王旭光,王志衡,吴福朝.Harris相关与特征匹配.模式识别与人工智能,2009,22(4): 505-513) [19] Wu Fuchao,Wang Zhiheng,Wang Xuguang. Feature Vector Field and Feature Matching. Pattern Recognition,2010,43(10): 3273-3281 [20] Liu Hongmin,Wang Zhiheng,Deng Chao. Extend Point Descriptors for Line,Curve and Region Matching // Proc of the International Conference on Machine Learning and Cybernetics. Qingdao,China,2010,I: 214-219 [21] Fan Bin,Wu Fuchao,Hu Zhanyi. Aggregating Gradient Distribution into Intensity Orders: A Novel Local Image Descriptor // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence,USA,2011: 2377-2384 [22] Wang Zhenhua,Fan Bin,Wu Fuchao. Local Intensity Order Pattern for Feature Description // Proc of the IEEE Conference on Computer Vision. Barcelona,Spain,2011: 603-610 [23] Canny J. A Computational Approach to Edge Detection. IEEE Trans on Pattern Analysis and Machine Intelligence,1986,8(6):679-698 [24] Harris C,Stephens M J. A Combined Corner and Edge Detector // Proc of the 4th Alvey Vision Conference. Manchester,UK,1988: 147-151
2013, Vol. 26 
