Information Retrieval over Multimedia Documents

While there are many textual and image retrieval systems, few have explored the granularity of the retrieval unit and the use of all available information for retrieval. This paper presents our work on using textual and image retrieval, fusing the results and providing document retrieval that uses visual and textual information from documents. A query re nement technique is also shown that blurs the line between browsing and searching and integrates both into the same framework.

[1]  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.

[2]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[3]  John R. Smith,et al.  Searching for Images and Videos on the World-Wide Web , 1999 .

[4]  Shih-Fu Chang,et al.  Transform features for texture classification and discrimination in large image databases , 1994, Proceedings of 1st International Conference on Image Processing.

[5]  Rosalind W. Picard,et al.  Interactive Learning Using a "Society of Models" , 2017, CVPR 1996.

[6]  Kannan Ramchandran,et al.  Multimedia Analysis and Retrieval System (MARS) Project , 1996, Data Processing Clinic.

[7]  Sharad Mehrotra,et al.  Query reformulation for content based multimedia retrieval in MARS , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

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

[9]  Shih-Fu Chang,et al.  Tools and techniques for color image retrieval , 1996, Electronic Imaging.

[10]  Hsinchun Chen,et al.  A Parallel Computing Approach to Creating Engineering Concept Spaces for Semantic Retrieval: The Illinois Digital Library Initiative Project , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[12]  Thomas S. Huang,et al.  Supporting Ranked Boolean Similarity Queries in MARS , 1998, IEEE Trans. Knowl. Data Eng..

[13]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[14]  Shih-Fu Chang,et al.  Automated binary texture feature sets for image retrieval , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[15]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[16]  Sharad Mehrotra,et al.  Similarity Search Using Multiple Examples in MARS , 1999, VISUAL.

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

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

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

[20]  Makoto Miyahara,et al.  Mathematical Transform Of (R, G, B) Color Data To Munsell (H, V, C) Color Data , 1988, Other Conferences.

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

[22]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

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

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