Visual information retrieval using synthesized imagery

In this project (VIRSI) we investigate the promising content-based retrieval paradigm known as interactive search or relevance feedback, and aim to extend it through the use of synthetic imagery. In relevance feedback methods, the user himself is a key factor in the search process as he provides positive and negative feedback on the results, which the system uses to iteratively improve the set of candidate results. In our approach we closely integrate the generation of synthetic imagery in the relevance feedback process through a new fundamental paradigm: Artificial Imagination (AIm).

[1]  Mark J. Huiskes,et al.  Aspect-Based Relevance Learning for Image Retrieval , 2005, CIVR.

[2]  S LewMichael,et al.  Content-based multimedia information retrieval , 2006 .

[3]  Amarnath Gupta,et al.  Virage image search engine: an open framework for image management , 1996, Electronic Imaging.

[4]  A. Dale Magoun,et al.  Decision, estimation and classification , 1989 .

[5]  Bart Thomee,et al.  Relevance feedback: perceptual learning and retrieval in bio-computing, photos, and video , 2004, MIR '04.

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

[7]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

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

[9]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[10]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[11]  C. W. Therrien,et al.  Decision, Estimation and Classification: An Introduction to Pattern Recognition and Related Topics , 1989 .

[12]  Wei-Ying Ma,et al.  Learning a semantic space from user's relevance feedback for image retrieval , 2003, IEEE Trans. Circuits Syst. Video Technol..

[13]  Ofer M. Shir,et al.  Niching in evolution strategies , 2005, GECCO '05.

[14]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

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

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

[17]  Andrew Calway,et al.  Proceedings of the IEEE International Conference on Image Processing , 1996 .

[18]  Shih-Fu Chang,et al.  Semantic visual templates: linking visual features to semantics , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[19]  Bir Bhanu,et al.  Integrating relevance feedback techniques for image retrieval using reinforcement learning , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Ramesh C. Jain,et al.  ACM SIGMM retreat report on future directions in multimedia research , 2005, TOMCCAP.

[21]  Wei-Ying Ma,et al.  Learning and inferring a semantic space from user's relevance feedback for image retrieval , 2002, MULTIMEDIA '02.

[22]  Paul A. Viola,et al.  Boosting Image Retrieval , 2004, International Journal of Computer Vision.

[23]  Marco La Cascia,et al.  Mix and Match Features in the ImageRover Search Engine , 2001, Principles of Visual Information Retrieval.

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

[25]  Guodong Guo,et al.  Boosting for content-based audio classification and retrieval: an evaluation , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..