Intensive Use of Correspondence Analysis for Large Scale Content-Based Image Retrieval

In this paper, we investigate the intensive use of Correspondence Analysis (CA) for large scale content-based image retrieval. Correspondence Analysis is a useful method for analyzing textual data and we adapt it to images using the SIFT local descriptors. CA is used to reduce dimensions and to limit the number of images to be considered during the search step. An incremental algorithm for CA is proposed to deal with large databases giving exactly the same result as the standard algorithm. We also integrate the Contextual Dissimilarity Measure in our search scheme in order to improve response time and accuracy. We explore this integration in two ways: (i) off-line (the structure of image neighborhoods is corrected off-line) and (ii) on-the-fly (the structure of image neighborhoods is adapted during the search). The evaluation tests have been performed on a large image database (up to 1 million images).

[1]  Andrew Zisserman,et al.  Automated location matching in movies , 2003, Comput. Vis. Image Underst..

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

[3]  Patrick Gros,et al.  Robust content-based image searches for copyright protection , 2003, MMDB '03.

[4]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[5]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[7]  Patrick Gros,et al.  Content-based Retrieval Using Local Descriptors: Problems and Issues from a Database Perspective , 2001, Pattern Analysis & Applications.

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

[9]  Cordelia Schmid,et al.  Effient Matching with Invariant Local Descriptors , 1998, SSPR/SPR.

[10]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[11]  Cordelia Schmid,et al.  A contextual dissimilarity measure for accurate and efficient image search , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Lixin Fan,et al.  Categorizing Nine Visual Classes using Local Appearance Descriptors , 2004 .

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

[14]  Andrew Zisserman,et al.  Scene Classification Via pLSA , 2006, ECCV.

[15]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  R. Clarke,et al.  Theory and Applications of Correspondence Analysis , 1985 .

[17]  Luc Van Gool,et al.  Content-Based Image Retrieval Based on Local Affinely Invariant Regions , 1999, VISUAL.

[18]  Louis Vuurpijl,et al.  Using Pen-Based Outlines for Object-Based Annotation and Image-Based Queries , 1999, VISUAL.

[19]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

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

[21]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[22]  Antti Oulasvirta,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[23]  Annie Morin,et al.  Une nouvelle approche pour la recherche d'images par le contenu , 2008, EGC.

[24]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[25]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[27]  James Demmel,et al.  LAPACK Users' Guide, Third Edition , 1999, Software, Environments and Tools.

[28]  C. Schmid,et al.  Indexing based on scale invariant interest points , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

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

[30]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[31]  Jack Dongarra,et al.  LAPACK Users' Guide, 3rd ed. , 1999 .

[32]  Jack Dongarra,et al.  LAPACK Users' guide (third ed.) , 1999 .

[33]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[34]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[35]  Rainer Lienhart,et al.  PLSA on Large Scale Image Databases , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[36]  R. Casey,et al.  Advances in Pattern Recognition , 1971 .

[37]  A. Morin.  Intensive use of correspondence analysis for information retrieval , 2004, 26th International Conference on Information Technology Interfaces, 2004..

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

[39]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[40]  M. Greenacre.  Correspondence analysis in practice , 1993 .