Relevance maximizing, iteration minimizing,...

Details Relevance maximizing, iteration minimizing,... Relevance maximizing, iteration minimizing,...

2006-09-26

2006-09-26

Metjahic, Safet (Department: 2161)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000, C707S793000, C707S793000, C707S793000, C707S793000

Reexamination Certificate

active

07113944

ABSTRACT:
An implementation of a technology, described herein, for relevance-feedback, content-based facilitating accurate and efficient image retrieval minimizes the number of iterations for user feedback regarding the semantic relevance of exemplary images while maximizing the resulting relevance of each iteration. One technique for accomplishing this is to use a Bayesian classifier to treat positive and negative feedback examples with different strategies. In addition, query refinement techniques are applied to pinpoint the users' intended queries with respect to their feedbacks. These techniques further enhance the accuracy and usability of relevance feedback. This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

REFERENCES:
patent: 5442778 (1995-08-01), Pedersen et al.
patent: 5619709 (1997-04-01), Caid et al.
patent: 5751286 (1998-05-01), Barber et al.
patent: 5855015 (1998-12-01), Shoham
patent: 5873056 (1999-02-01), Liddy et al.
patent: 5889506 (1999-03-01), Lopresti et al.
patent: 5893095 (1999-04-01), Jain et al.
patent: 5899999 (1999-05-01), De Bonet
patent: 5987457 (1999-11-01), Ballard
patent: 6020955 (2000-02-01), Messina
patent: 6038560 (2000-03-01), Wical
patent: 6094652 (2000-07-01), Faisal
patent: 6134532 (2000-10-01), Lazarus et al.
patent: 6175829 (2001-01-01), Li et al.
patent: 6189002 (2001-02-01), Roitblat
patent: 6282549 (2001-08-01), Hoffert et al.
patent: 6304864 (2001-10-01), Liddy et al.
patent: 6311194 (2001-10-01), Sheth et al.
patent: 6345274 (2002-02-01), Zhu et al.
patent: 6347313 (2002-02-01), Ma et al.
patent: 6510406 (2003-01-01), Marchisio
patent: 6523026 (2003-02-01), Gillis
patent: 6553385 (2003-04-01), Johnson et al.
patent: 6567797 (2003-05-01), Schuetze et al.
patent: 6675159 (2004-01-01), Lin et al.
patent: 6687696 (2004-02-01), Hofmann et al.
patent: 6760714 (2004-07-01), Caid et al.
patent: 6766316 (2004-07-01), Caudill et al.
patent: 6766320 (2004-07-01), Wang et al.
patent: 2002/0038299 (2002-03-01), Zernik et al.
patent: 2002/0194200 (2002-12-01), Flank et al.
patent: 2003/0028512 (2003-02-01), Stensmo
patent: 2004/0111408 (2004-06-01), Caudill et al.
Z. Chen et al., “Web Mining for Web Image Retrieval” Journal of the American Society for Information Science and Technology 52(10) 15 pages, Aug. 2001.
R. Agrawal, et al., “Fast Discovery of Association Rules, ” In Advances in Knowledge Discovery and Data Mining, Fayyad Um, Piatetsky-Shaprio G. Smyth P & Uthurusamy R. (eds). AAAI Press, Menlo Park, California, (1994), pp. 307-328.
J. Allen, “Natural Language Understanding,” University of Rochester, 1994, pp. 23-25.
D. Bikel, et al., “Nymble: A High-Performance Learning Name-Finder,” Proc. of the Fifth Conference on Applied Natural Language Processing, Association of Computational Linguistics, 1997, pp. 194-201.
M. Flickner et al., “Query by Image and Video Content: The QBIC System,” IEEE Computer, Sep. 1995, pp. 23-32.
D. Harman, et al., “Inverted Files,” Information Retrieval; Data Structures and Algorithms, Frakes WB and Baeza-Yates R (eds), 1992, Chapter 3, Prentice Hall, NY.
E. Horvitz et al., The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users: Proc. of the 14th Conference on Uncertainty in Articial Intelligence, 1998.
T. Joachims, “A Probablistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization”, in Proc. of the 14th International Conference on Machine Learning, Nashville, TN, Jul. 1997, pp. 143-151. Morgan Kaufmann Publisher, San Francisco, CA.
J-H Kim, et al., “A Rule-Based Named Entity Recognitioin System for Speech Input,” in Proc. of the Sixth International Conference on Spoken Language Processing, 2000, vol. 1, pp. 528-531.
N. Kosugi, et al., “Practical Query-By-Humming System” Proc. of the 8th ACM International Conference on Multimedia, 2000, pp. 333-342.
Y. Lu et al., “A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems,” Proc. of the 8th ACM International Conference on Multimedia, 2000, pp. 31-38.
T. M. Mitchell, “Machine Learning,” 1997, pp. 176-183, McGraw-Hill.
M. F. Porter, “An Algorithm of Suffix Stripping,” Program, vol. 14, No. 3, pp. 130-137, Jul. 1980.
C. J. van Rijsbergen, “Information Retrieval,” Butterworths, Department of Computing Science, University of Glasgow, 1979.
Shen et al., “Giving Meanings to WWW Images”, Proc. of the 8th ACM International Conference on Multimedia, 2000, pp. 39-48.
Gong, et al., “An Image Database System with Content Capturing and Fast Image Indexing Abilities,” Proceedings of IEEE International Conference on Multimedia Computing and Systems, 1994, pp. 121-130.
A. Ono, A Flexible Content-Based Image Retrieval System with Combined Scene Description Keywork, Proceedings of IEEE International Conference on Multimedia Computing and Systems, 1996, pp. 201-208.
Zhang et al., “A Scheme of Visual Feature Based Image Indexing,” To appear in SPIE Conference on Storage and Retrieval for Image and Video Databases, San Jose, CA, Feb. 1995, pp. 1-12.

Affiliated with

Also associated with

Rating

Relevance maximizing, iteration minimizing,... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Relevance maximizing, iteration minimizing,..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Relevance maximizing, iteration minimizing,... will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3590077