Local Keypoints and Global Affine Geometry: Triangles and Ellipses for Image Fragment Matching

Image matching and retrieval is one of the most important areas of computer vision. The key objective of image matching is detection of near-duplicate images. This chapter discusses an extension of this concept, namely, the retrieval of near-duplicate image fragments. We assume no a’priori information about visual contents of those fragments. The number of such fragments in an image is also unknown. Therefore, we address the problem and propose the solution based purely on visual characteristics of image fragments The method combines two techniques: a local image analysis and a global geometry synthesis. In the former stage, we analyze low-level image characteristics, such as local intensity gradients or local shape approximations. In the latter stage, we formulate global geometrical hypotheses about the image contents and verify them using a probabilistic framework.

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

[2]  Luc Van Gool,et al.  Moment invariants for recognition under changing viewpoint and illumination , 2004, Comput. Vis. Image Underst..

[3]  Jean-Michel Morel,et al.  ASIFT: A New Framework for Fully Affine Invariant Image Comparison , 2009, SIAM J. Imaging Sci..

[4]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[5]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Andrzej Sluzek,et al.  A vision-based technique for assisting visually impaired people and autonomous agents , 2010, 3rd International Conference on Human System Interaction.

[7]  章 毓晋 Semantic-based visual information retrieval , 2007 .

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

[9]  H. C. Corben,et al.  Classical Mechanics (2nd ed.) , 1961 .

[10]  Yun Zhang,et al.  A Novel Interest-Point-Matching Algorithm for High-Resolution Satellite Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Md. Saiful Islam,et al.  Relative scale method to locate an object in cluttered environment , 2008, Image Vis. Comput..

[12]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Yu-Jin Zhang Semantic-based visual information retrieval , 2006 .

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

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

[16]  H. Goldstein,et al.  Classical Mechanics , 1951, Mathematical Gazette.

[17]  Juho Kannala,et al.  Algorithms for Computing a Planar Homography from Conics in Correspondence , 2006, BMVC.

[18]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[19]  Langis Gagnon,et al.  Key-Places Detection and Clustering in Movies Using Latent Aspects , 2007, 2007 IEEE International Conference on Image Processing.

[20]  Aly A. Farag,et al.  CSIFT: A SIFT Descriptor with Color Invariant Characteristics , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[21]  Liang-Tien Chia,et al.  Image near-duplicate retrieval using local dependencies in spatial-scale space , 2008, ACM Multimedia.

[22]  Yan Ke,et al.  An efficient parts-based near-duplicate and sub-image retrieval system , 2004, MULTIMEDIA '04.

[23]  Chong-Wah Ngo,et al.  Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection , 2009, IEEE Transactions on Image Processing.

[24]  Wei Zhang,et al.  Image Based Localization in Urban Environments , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[25]  Hung-Khoon Tan,et al.  Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning , 2007, IEEE Transactions on Multimedia.

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

[27]  Mubarak Shah,et al.  Two-frame wide baseline matching , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

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

[30]  Andrzej Sluzek,et al.  Detection of Image Fragments Related by Affine Transforms: Matching Triangles and Ellipses , 2010, 2010 International Conference on Information Science and Applications.

[31]  Hans P. Moravec Rover Visual Obstacle Avoidance , 1981, IJCAI.

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