Interaction Models and Relevance Feedback in Image Retrieval

Human-computer interaction is increasingly recognised to be an indispensable component of image retrieval systems. A typical form of interaction is that of relevance feedback whereby users supply relevance information on the retrieved images. This information can subsequently be used to optimise retrieval parameters. The first part of the chapter provides a comprehensive review of existing relevance feedback techniques and also discusses a number of limitations that can be addressed more successfully in a browsing framework. Browsing models form the focus of the second part of this chapter where we will evaluate the merit of hierarchical structures and networks for interactive image search. This exposition aims to provide enough detail to enable the practitioner to implement many of the techniques and to find numerous pointers to the relevant literature otherwise.

[1]  Minh N. Do,et al.  Integrated Browsing and Searching of Large Image Collections , 2000, VISUAL.

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[4]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[5]  Wei-Ying Ma,et al.  Learning an image manifold for retrieval , 2004, MULTIMEDIA '04.

[6]  John C. Dalton,et al.  Hierarchical browsing and search of large image databases , 2000, IEEE Trans. Image Process..

[7]  Wei-Ying Ma,et al.  A probabilistic semantic model for image annotation and multi-modal image retrieval , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  David A. Cohn,et al.  Improving generalization with active learning , 1994, Machine Learning.

[9]  Tsuhan Chen,et al.  Feature space warping: an approach to relevance feedback , 2002, Proceedings. International Conference on Image Processing.

[10]  Stefan M. Rüger,et al.  Automated Image Annotation Using Global Features and Robust Nonparametric Density Estimation , 2005, CIVR.

[11]  Simone Santini,et al.  Emergent Semantics through Interaction in Image Databases , 2001, IEEE Trans. Knowl. Data Eng..

[12]  Richard H. Fowler,et al.  Information Navigator: An information system using associative networks for display and retrieval , 1992 .

[13]  Kriengkrai Porkaew,et al.  Query refinement for multimedia similarity retrieval in MARS , 1999, MULTIMEDIA '99.

[14]  Jingrui He,et al.  Mean version space: a new active learning method for content-based image retrieval , 2004, MIR '04.

[15]  Joo-Hwee Lim,et al.  Learning Similarity Matching in Multimedia Content-Based Retrieval , 2001, IEEE Trans. Knowl. Data Eng..

[16]  Bir Bhanu,et al.  Probabilistic Feature Relevance Learning for Content-Based Image Retrieval , 1999, Comput. Vis. Image Underst..

[17]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[18]  William Stafford Noble,et al.  Support vector machine , 2013 .

[19]  Simone Santini,et al.  Integrated browsing and querying for image databases , 2000, IEEE MultiMedia.

[20]  Boon-Lock Yeo,et al.  Video visualization for compact presentation and fast browsing of pictorial content , 1997, IEEE Trans. Circuits Syst. Video Technol..

[21]  Stephen Wolfram,et al.  A New Kind of Science , 2003, Artificial Life.

[22]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[23]  B. S. Manjunath,et al.  Adaptive nearest neighbor search for relevance feedback in large image databases , 2001, MULTIMEDIA '01.

[24]  Christos Faloutsos,et al.  FALCON: Feedback Adaptive Loop for Content-Based Retrieval , 2000, VLDB.

[25]  H. J. Zhang,et al.  IMPROVING CBIR BY SEMANTIC PROPAGATION AND CROSS MODALITY QUERY EXPANSION , 2001 .

[26]  Sugata Ghosal,et al.  An image retrieval system with automatic query modification , 2002, IEEE Trans. Multim..

[27]  C. Tomasi.  The Earth Mover's Distance, Multi-Dimensional Scaling, and Color-Based Image Retrieval , 1997 .

[28]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[29]  R. Manmatha,et al.  A Model for Learning the Semantics of Pictures , 2003, NIPS.

[30]  Deok-Hwan Kim,et al.  QCluster: relevance feedback using adaptive clustering for content-based image retrieval , 2003, SIGMOD '03.

[31]  HongJiang Zhang,et al.  Scheme for visual feature-based image indexing , 1995, Electronic Imaging.

[32]  Christopher C. Yang.  Content-Based Image Retrieval: A Comparison between Query by Example and Image Browsing Map Approaches , 2004, J. Inf. Sci..

[33]  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).

[34]  Fabio Roli,et al.  Bayesian relevance feedback for content-based image retrieval , 2004, Pattern Recognit..

[35]  Janghyun Yoon,et al.  Relevance feedback for semantics based image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[36]  Thomas S. Huang,et al.  One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[37]  Bo Zhang,et al.  Learning in Region-Based Image Retrieval , 2003, CIVR.

[38]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[39]  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).

[40]  Minerva M. Yeung,et al.  Efficient matching and clustering of video shots , 1995, Proceedings., International Conference on Image Processing.

[41]  Ronald Fagin,et al.  Efficient similarity search and classification via rank aggregation , 2003, SIGMOD '03.

[42]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[43]  Gerard Salton,et al.  The SMART Retrieval System , 1971 .

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

[45]  Thierry Pun,et al.  Strategies for positive and negative relevance feedback in image retrieval , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[46]  Chaomei Chen,et al.  Similarity-Based Image Browsing , 2000 .

[47]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[48]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[49]  Jana Urban,et al.  AN ADAPTIVE APPROACH TOWARDS CONTENT-BASED IMAGE RETRIEVAL , 2003 .