RETIN: A Content-Based Image Indexing and Retrieval System

Abstract:This paper presents RETIN, a new system for automatic image indexing and interactive content-based image retrieval. The most original aspect of our work rests on the distance computation and its adjustment by relevance feedback. First, during an offline stage, the indexes are computed from attribute vectors associated with image pixels. The feature spaces are partitioned through an unsupervised classification, and then, thanks to these partitions, statistical distributions are processed for each image. During the online use of the system, the user makes an iconic request, i.e. he brings an example of the type of image he is looking for. The query may be global or partial, since the user can reduce his request to a region of interest. The comparison between the query distribution and that of every image in the collection is carried out by using a weighted dissimilarity function which manages the use of several attributes. The results of the search are then refined by means of relevance feedback, which tunes the weights of the dissimilarity measure via user interaction. Experiments are then performed on large databases and statistical quality assessment shows the good properties of RETIN for digital image retrieval. The evaluation also shows that relevance feedback brings flexibility and robustness to the search.

[1]  Christophe Biernacki,et al.  Indexation et appariement d'images par modèle de mélange gaussien des couleurs , 1999 .

[2]  Neill W. Campbell,et al.  Iterative refinement by relevance feedback in content-based digital image retrieval , 1998, MULTIMEDIA '98.

[3]  John R. Smith,et al.  Image Classification and Querying Using Composite Region Templates , 1999, Comput. Vis. Image Underst..

[4]  M. Mitschke,et al.  Eecient Query Reenement for Image Retrieval , 1998 .

[5]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Other Conferences.

[6]  D. Koubaroulis,et al.  Robust Image Retrieval in a Statistical , 1999 .

[7]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Carlo Tomasi,et al.  Perceptual metrics for image database navigation , 1999 .

[9]  Gerard Salton,et al.  Improving retrieval performance by relevance feedback , 1997, J. Am. Soc. Inf. Sci..

[10]  Thierry Pun,et al.  Hunting moving targets: extension to Bayesian methods in multimedia databases , 1999, Optics East.

[11]  Nozha Boujemaa,et al.  Region Queries without Segmentation for Image Retrieval by Content , 1999, VISUAL.

[12]  Ingemar J. Cox,et al.  An optimized interaction strategy for Bayesian relevance feedback , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[13]  Nozha Boujemaa,et al.  Surfimage: a flexible content-based image retrieval system , 1998, MULTIMEDIA '98.

[14]  Luc Van Gool,et al.  Recognizing color patterns irrespective of viewpoint and illumination , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  John P. Oakley,et al.  Storage and Retrieval for Image and Video Databases , 1993 .

[16]  Thijs Westerveld,et al.  Image Retrieval: Content versus Context , 2000, RIAO.

[17]  Thierry Pun,et al.  Content-based query of image databases: inspirations from text retrieval , 2000, Pattern Recognit. Lett..

[18]  Jean-Michel Jolion,et al.  Image indexation and content based search using pre-attentive similarities , 2000, RIAO.

[19]  Chahab Nastar,et al.  Relevance feedback and category search in image databases , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[20]  Mihai Datcu,et al.  Interactive learning and probabilistic retrieval in remote sensing image archives , 2000, IEEE Trans. Geosci. Remote. Sens..

[21]  Thierry Pun,et al.  Performance evaluation in content-based image retrieval: overview and proposals , 2001, Pattern Recognit. Lett..

[22]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[23]  Chahab Nastar.  The Image Shape Spectrum for Image Retrieval , 1997 .

[24]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[25]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Multimedia Systems.

[26]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Electronic Imaging.

[27]  Eric J. Pauwels,et al.  Finding Salient Regions in Images: Nonparametric Clustering for Image Segmentation and Grouping , 1999, Comput. Vis. Image Underst..

[28]  Tom Minka,et al.  Interactive learning with a "society of models" , 1997, Pattern Recognit..

[29]  C. Schmid,et al.  Matching by local invariants , 1995 .

[30]  Alberto Del Bimbo,et al.  Visual Image Retrieval by Elastic Matching of User Sketches , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Haim Schweitzer,et al.  Organizing image databases as visual-content search trees , 1999, Image Vis. Comput..

[32]  T.S. Huang,et al.  A relevance feedback architecture for content-based multimedia information retrieval systems , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[33]  Remco C. Veltkamp,et al.  Content-based image retrieval systems: A survey , 2000 .

[34]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[35]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[36]  Joshua R. Smith,et al.  Image retrieval evaluation , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

[37]  Jitendra Malik,et al.  Blobworld: A System for Region-Based Image Indexing and Retrieval , 1999, VISUAL.

[38]  Ramesh Jain,et al.  Storage and Retrieval for Image and Video Databases III , 1995 .

[39]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[40]  Peter N. Yianilos,et al.  Data structures and algorithms for nearest neighbor search in general metric spaces , 1993, SODA '93.

[41]  Chahab Nastar,et al.  Efficient query refinement for image retrieval , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[42]  Donald Geman,et al.  A Stochastic Feedback Model for Image Retrieval , 1999 .

[43]  Benoit Huet,et al.  Relational histograms for shape indexing , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).