Local image descriptor based on spectral embedding

This study presents a local image descriptor based on spectral embedding. Specifically, the spectra of line graph are used to represent image edges, corners and edge points with big curvature. The authors theoretically analyse and experimentally verify that the spectra of line graph are robust to noise and are invariant to rotation and linear intensity changes. Based on such a fact, some local image descriptors are constructed using the spectra of line graph. Comparative experiments demonstrate the effectiveness of the proposed descriptor and its superiority to some state-of-the-art descriptors under image rotation, image blur, viewpoint change, illumination change, JPEG compression and noise.

[1]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[2]  Edwin R. Hancock,et al.  Spectral correspondence for point pattern matching , 2003, Pattern Recognit..

[3]  Jun Tang,et al.  A Laplacian spectral method for stereo correspondence , 2007, Pattern Recognit. Lett..

[4]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[5]  Yi Yang,et al.  Discovering Discriminative Graphlets for Aerial Image Categories Recognition , 2013, IEEE Transactions on Image Processing.

[6]  Jeffrey Mark Siskind,et al.  Image Segmentation with Ratio Cut , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jin Tang,et al.  A graph and PNN-based approach to image classification , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[8]  Bin Fan,et al.  Local Intensity Order Pattern for feature description , 2011, 2011 International Conference on Computer Vision.

[9]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[10]  Xueming Qian,et al.  PLBP: An effective local binary patterns texture descriptor with pyramid representation , 2011, Pattern Recognit..

[11]  Chris H. Q. Ding,et al.  A min-max cut algorithm for graph partitioning and data clustering , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[12]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[13]  Xiaoqin Zhang,et al.  Graph-Embedding-Based Learning for Robust Object Tracking , 2014, IEEE Transactions on Industrial Electronics.

[14]  Chong-Wah Ngo,et al.  Flip-Invariant SIFT for Copy and Object Detection , 2013, IEEE Transactions on Image Processing.

[15]  Tony Lindeberg,et al.  Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention , 1993, International Journal of Computer Vision.

[16]  Carey E. Priebe,et al.  Universally Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs , 2012, 1207.6745.

[17]  Nicu Sebe,et al.  Sparse Color Interest Points for Image Retrieval and Object Categorization , 2012, IEEE Transactions on Image Processing.

[18]  Mayank Bansal,et al.  Joint Spectral Correspondence for Disparate Image Matching , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[21]  Duanduan Yang,et al.  A low-dimensional local descriptor incorporating TPS warping for image matching , 2010, Image Vis. Comput..

[22]  Michael Brady,et al.  Feature-based correspondence: an eigenvector approach , 1992, Image Vis. Comput..

[23]  Shawn D. Newsam,et al.  Geographic Image Retrieval Using Local Invariant Features , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Richard M. Leahy,et al.  An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[26]  Lizhuang Ma,et al.  A new framework for feature descriptor based on SIFT , 2009, Pattern Recognit. Lett..

[27]  Leo Grady,et al.  Isoperimetric graph partitioning for image segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[29]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[30]  R. Karthik,et al.  Image Stitching with Combined Moment Invariants and Sift Features , 2013, ANT/SEIT.

[31]  Qiujuan Lv,et al.  Feature points extraction of different structure for industrial computed tomography image contour , 2013 .

[32]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Hongjian You,et al.  BFSIFT: A Novel Method to Find Feature Matches for SAR Image Registration , 2012, IEEE Geoscience and Remote Sensing Letters.

[35]  Olga Veksler,et al.  Image segmentation by nested cuts , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[36]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[37]  H. C. Longuet-Higgins,et al.  An algorithm for associating the features of two images , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[38]  Z. H. Wang,et al.  Feature vector field and feature matching , 2010, Pattern Recognit..

[39]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.