Data processing: database and file management or data structures – Data integrity
Reexamination Certificate
2008-10-03
2011-12-06
Dang, Thanh-Ha (Department: 2163)
Data processing: database and file management or data structures
Data integrity
C707S692000, C707S723000, C707S748000, C707S749000, C707S763000
Reexamination Certificate
active
08073818
ABSTRACT:
Described is a technology in which image near-duplicate retrieval is performed using similarities between patterns of query image words and patterns of database image words. In general, the image retrieval problems resulting from visual polysemy are reduced by using such visual patterns. Visual word vectors and visual pattern vectors are determined for the query image and a database image. These four vectors are used to determine similarity between the database image and the query image. The similarity scores may be used for ranking and/or re-ranking the database image similarity to the query image relative to other database images' similarity scores. Also described is expanding a query visual word of the query image to a set of visual words that are visual synonyms with the query visual word, to help reduce image retrieval problems resulting from visual synonymy.
REFERENCES:
patent: 5893095 (1999-04-01), Jain et al.
patent: 5899999 (1999-05-01), De Bonet
patent: 6415282 (2002-07-01), Mukherjea et al.
patent: 6624821 (2003-09-01), Shin et al.
patent: 6763148 (2004-07-01), Sternberg et al.
patent: 7035467 (2006-04-01), Nicponski
patent: 7106903 (2006-09-01), Chang et al.
patent: 7366718 (2008-04-01), Pugh et al.
patent: 7539657 (2009-05-01), Liu et al.
patent: 7644090 (2010-01-01), Nakano et al.
patent: 7734627 (2010-06-01), Tong
patent: 2004/0076345 (2004-04-01), Olszak et al.
patent: 2005/0084154 (2005-04-01), Li et al.
patent: 2005/0238209 (2005-10-01), Ikeda et al.
patent: 2006/0050953 (2006-03-01), Farmer et al.
patent: 2006/0257053 (2006-11-01), Boudreau et al.
patent: 2007/0078846 (2007-04-01), Gulli et al.
patent: 2008/0027917 (2008-01-01), Mukherjee et al.
patent: 2008/0162474 (2008-07-01), Thong et al.
patent: 2009/0208097 (2009-08-01), Husseini et al.
patent: 2009/0220166 (2009-09-01), Choi et al.
patent: 2009/0234831 (2009-09-01), Ebadollahi et al.
Ahonen-Myka, et al., “Data Mining Meets Collocations Discovery”, CSLI Studies in Computational Linguistics Online, 2005, pp. 194-203.
Celik, et al., “Zonal Co-Location Pattern Discovery with Dynamic Parameters”, Seventh IEEE International Conference on Data Mining, 2007, pp. 433-438.
Chen, et al., “Similarity-Based Image Browsing”, 2000, 8 pages.
Zhang, et al., “Detecting Image Near-Duplicate by Stochastic Attribute Relational Graph Matching with Learning”, ACM Multimedia, 8 pages.
Csurka, et al., “Visual Categorization with Bags of Keypoints”, 2004, 16 pages.
Fergus, et al., “Object Class Recognition by Unsupervised Scale-Invariant Learning”, In CVPR, 2003, 8 pages.
Crandall, et al., “Spatial Priors for Part-Based Recognition Using Statistical Models”, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)—vol. 1, 2005, pp. 10-17.
Mikolajczyk, et al., “A Comparison of Affine Region Detectors”, International Journal of Computer Vision, vol. 65 , Issue 1-2, Nov. 2005, pp. 43-72.
Matas, et al., “Robust Wide-Baseline Stereo from Maximally Stable Extremal Regions”, In British Machine Vision Conference, BMVC, 2002, pp. 384-393.
Bay, et al., “SURF: Speeded Up Robust Features”, Proceedings of the ninth European Conference on Computer Vision, ECCV 2006, 14 pages.
Sivic, et al., “Video Data Mining Using Configurations of Viewpoint Invariant Regions”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04)—vol. 1, 2004, pp. 488-495.
Quack, et al., “Video Mining with Frequent Itemset Configurations”, CIVR 2006, pp. 1-10.
Huang, et al., “Discovery of Collocation Patterns: From Visual Words to Visual Phrases”, Aug. 23, 2002, 29 pages.
Quack, et al., “Efficient Mining of Frequent and Distinctive Feature Configurations”, IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1-8.
Yuan, et al., “Discovering Co-Location Patterns from Spatial Datasets: A General Approach”, IEEE, 2007, 8 pages.
David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, Accepted for publication in the International Journal of Computer Vision, Jan. 5, 2004, pp. 1-28.
Yang, et al., “Evaluating Bag-of-Visual-Words Representations in Scene Classification”, Proceedings of the international workshop on Workshop on multimedia information retrieval, 2007, pp. 197-206.
Sivic, et al., “Video Google: A Text Retrieval Approach to Object Matching in Videos”, Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV 2003) 2-Volume Set, 2003, pp. 1-8.
Yang, et al., “A Text Categorization Approach to Video Scene Classification Using Keypoint Features”, Oct. 3, 2006, pp. 1-25.
Philbin, et al., “Object Retrieval with Large Vocabularies and Fast Spatial Matching”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2007, 8 pages.
“Zurich Building Image Database”, http://www.vision.ee.ethz.ch/showroom/zubud/index.en.html, May 27, 2008.
Shao, et al., “Fast Indexing for Image Retrieval Based on Local Appearance with Re-Ranking”, International Conference on Image Processing, vol. 3, Issue, 2003, pp. 14-17.
Agrawal, et al., “Mining Association Rules between Sets of Items in Large Database”, Proceedings of the 1993 ACM SIGMOD Conference, May 1993, pp. 1-10.
Nister, et al., “Scalable Recognition with a Vocabulary Tree”, Conference on Computer Vision and Pattern Recognition, 2006 IEEE Computer Society, vol. 2, pp. 2161-2168.
Mitra, et al., “An Analysis of Statistical and Syntactic Phrases”, Metadata Version 1, 1997, pp. 1-11.
Han, et al., “Frequent Pattern Mining: Current Status and Future Directions”, Springer, Data Min Knowl Disc (2007), Jan. 27, 2007, pp. 55-86.
Salton, et al., “A Vector Space Model for Automatic Indexing”, Communications of the ACM, vol. 18, No. 11, Nov. 1975, pp. 613-620.
Yasuhiko Morimoto, “Mining Frequent Neighboring Class Sets in Spatial Databases”, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001, pp. 353-358.
Duan Manni
Xie Xing
Dang Thanh-Ha
Kelly Joseph R.
Microsoft Corporation
Westman Champlin & Kelly P.A.
LandOfFree
Co-location visual pattern mining for near-duplicate image... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Co-location visual pattern mining for near-duplicate image..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Co-location visual pattern mining for near-duplicate image... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4309445