Visual information retrieval from large distributed online repositories

Digital images and video are becoming an integral part of human communications. The ease of capturing and creating digital images has caused most on-line information sources look more “visual”. We use more and more visual content in expressing ideas, reporting, education, and entertainment. With the tremendous amount of visual information becoming on-line, how does one find visual information from distributed repositories efficiently, at least to the same extent as that of existing information retrieval systems. With the growing number of on-line users, how does one design a system with performance scalable to a large extent?

[1]  Clifford A. Lynch,et al.  The Z39.50 Information Retrieval Standard: Part I: A Strategic View of Its Past, Present and Future , 1997, D-Lib Magazine.

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

[3]  Harpreet S. Sawhney,et al.  Model-based 2D&3D dominant motion estimation for mosaicing and video representation , 1995, Proceedings of IEEE International Conference on Computer Vision.

[4]  Shih-Fu Chang,et al.  Visually Searching the Web for Content , 1997, IEEE Multim..

[5]  Thomas P. Minka,et al.  An image database browser that learns from user interaction , 1996 .

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

[7]  Boon-Lock Yeo,et al.  Video browsing using clustering and scene transitions on compressed sequences , 1995, Electronic Imaging.

[8]  Shih-Fu Chang,et al.  VideoQ: an automated content based video search system using visual cues , 1997, MULTIMEDIA '97.

[9]  Behzad Shahraray,et al.  Automatic generation of pictorial transcripts of video programs , 1995, Electronic Imaging.

[10]  Elaine Svenonius,et al.  Title Page Sanctity? The Distribution of Access Points in a Sample of English Language Monographs , 1986 .

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

[12]  Hayit Greenspan,et al.  Finding Pictures of Objects in Large Collections of Images , 1996, Object Representation in Computer Vision.

[13]  Sara Shatford,et al.  Analyzing the Subject of a Picture: A Theoretical Approach , 1986 .

[14]  Rakesh Mohan,et al.  Text-based search of TV news stories , 1996, Other Conferences.

[15]  Alexander G. Hauptmann,et al.  Text, Speech, and Vision for Video Segmentation: The InformediaTM Project , 1995 .

[16]  Shih-Fu Chang,et al.  Compressed-domain techniques for image/video indexing and manipulation , 1995, Proceedings., International Conference on Image Processing.

[17]  Shih-Fu Chang,et al.  MetaSEEk: a content-based metasearch engine for images , 1997, Electronic Imaging.

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

[19]  Stuart Weibel,et al.  Image Description on the Internet: A Summary of the CNI/OCLC Image Metadata Workshop September 24 - 25, 1996, Dublin, Ohio , 1997, D Lib Mag..

[20]  Michael J. Swain,et al.  WebSeer: An Image Search Engine for the World Wide Web , 1996 .

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

[22]  Adele E. Howe,et al.  Experiences with selecting search engines using metasearch , 1997, TOIS.