Relevance Feedback in Content-based Image Search

Content-based image retrieval (CBIR) is a research area dedicated to address the retrieve and search multimedia documents for digital libraries. Relevance feedback is a powerful technique in CBIR and has been an active research topic for the past few years. In this paper, we review the current state-of-the-art of research on relevance feedbacks for CBIR and present the iFind system developed at Microsoft Research China equipped with a set of powerful relevance feedback algorithms. We also provide an outlook on the remaining research issues in CBIR, especially on applying learning and data mining technologies in search of multimedia data on the Web.

[1]  Myron Flickner,et al.  Query by Image and Video Content , 1995 .

[2]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[3]  Shi-Kuo Chang,et al.  Image Information Systems: Where Do We Go From Here? , 1992, IEEE Trans. Knowl. Data Eng..

[4]  Mingjing Li,et al.  Web mining for Web image retrieval , 2001, J. Assoc. Inf. Sci. Technol..

[5]  S. Sclaroff,et al.  ImageRover: a content-based image browser for the World Wide Web , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

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

[7]  Wei-Ying Ma,et al.  Information embedding based on user's relevance feedback for image retrieval , 1999, Optics East.

[8]  Thomas S. Huang,et al.  A novel relevance feedback technique in image retrieval , 1999, MULTIMEDIA '99.

[9]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[10]  Robert M. Losee,et al.  Feedback in Information Retrieval. , 1996 .

[11]  Qiang Yang,et al.  A unified framework for semantics and feature based relevance feedback in image retrieval systems , 2000, ACM Multimedia.

[12]  Gerard Salton,et al.  Optimization of relevance feedback weights , 1995, SIGIR '95.

[13]  Thomas S. Huang,et al.  Content-based image retrieval with relevance feedback in MARS , 1997, Proceedings of International Conference on Image Processing.

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

[15]  Nuno Vasconcelos,et al.  A Bayesian framework for content-based indexing and retrieval , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).