Image analysis – Pattern recognition – Classification
Reexamination Certificate
2007-07-10
2007-07-10
Sherali, Ishrat (Department: 2624)
Image analysis
Pattern recognition
Classification
C382S159000, C382S225000
Reexamination Certificate
active
11129090
ABSTRACT:
A method is provided for evaluating identity of an object, the method including: converting feature information representing the object to a plurality of mathematically defined components; grouping the components into multiple modalities; producing respective first prediction information for each respective modality wherein the respective prediction information for each respective modality is based upon respective components grouped into that respective modality; and producing second prediction information based upon the respective first prediction information produced for the multiple respective modalities.
REFERENCES:
patent: 6944319 (2005-09-01), Huang et al.
patent: 2003/0169908 (2003-09-01), Kim et al.
patent: 2004/0022442 (2004-02-01), Kim
patent: 2004/0136574 (2004-07-01), Kozakaya et al.
patent: 2005/0100209 (2005-05-01), Lewis et al.
patent: 2005/0265607 (2005-12-01), Chang
“Combining multiple classifiers by averaging or by multiplying?” by Tax et al. (“Tax”) Pattern Recognition, 33(9):1475-1485, 2000.
Smaragdis et al., “Audio/Visual Independent Components”, 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), Apr. 2003, pp. 709-714, Nara, Japan.
Flickner et al., “Query by Image and Video Content: the QBIC System”, 11th Annual Computer Security Applications Conference, Sep. 1995, pp. 23-32.
Tax et al., “Combining Multiple Classifiers by Averaging or Multiplying?”, Pattern Recognition, The Journal of Pattern Recognition Society, Jun. 1999, pp. 1475-1485, 33 (2000), Elsevier Science Ltd.
Fagin et al., “Optimal Aggregation Algorithms for Middleware”, Journal of Computer and System Sciences, Apr. 2002, pp. 614-656, 66 (2003), Elsevier Science Ltd.
Rui et al., “Image Retrieval: Past, Present, and Future” International Symposium on Multimedia Information Processing, 1997.
Hershey et al., “Audio-Vision: Using Audio-Visual Synchrony to Locate Sounds”, Advances in Neural Information Processing Systems 12, MIT Press, Cambridge MA, 2001.
Hansen et al., “On Independent Component Analysis for Multimedia Signals”, Multimedia Image and Video Processing, CRC Press, 2000.
Ding et al., “A Min-max Cut Algorithm for Graph Partitioning and Data Clustering”, IEEE International Conference on Data Mining, pp. 107-114, 2001.
Vinokourov et al.; “Intferring a Semantic Representation of Text Via Cross-Language Correlation Analysis”, In Advances of Neural Information Processing, 2002.
Westerveld, “Image Retrieval: Content Versus Context”, Content-Based Multimedia Information Access, RIAO, 2000.
Yan et al., “The Combination Limit in Multimedia Retrieval”, ACM Multimedia, 2003.
Wu et al., “Optimal Multimodal Fusion for Multimedia data Analysis”, 2004, ACM, New York, New York.
Vinokourov, et al., “Learning the Semantics of Multimedia Content with Application to Web Image Retrieval and Classification”, in Proceedings of Fourth International Symposium on Independent Component Analysis and Blind Source Separation, 2003.
Fisher et al., “Learning Joint Statistical Models for Audio-Visual Fusion and Segregation”, Advances in Neural Information Processing Systems 13, MIT Press, Cambridge MA, 2000.
Cascia, et al., “Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web”, IEEE Workshop on Content-based Access of Image and Video Libraries, 1998.
Velivelli et al., “Detection of Documentary Scene Changes by Audio-Visual Fusion”, In proceedings of Iriternational conference on Image and video retrieval, 2003.
Beyer et al., “When is “Nearest Neighbor” Meaningful?”, International Conference on Database Theory, pp. 217-235, 1999.
Donoho, “High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality”, American Math. Society Lecture—Match Challenges of the 21rst Century, 2000.
Goh et al., “SVM Binary Classifier Ensembles for Image Classification”, ACM International Conference on Information and Knowledge Management (CIKM), 2001.
Kolenda, et al., “Independent Componenet Analysis for Understanding Multimedia Content”, In Proc. of IEEE Workshop on Neural Networks for Signal Processing, 2002.
Adams et al., “IBM Research TREC-2002 Video Retrieval System” 2002, New York, New York.
Bartlett et al., “Independent Component Representation for Face Recognition”, SPIE Conf. on Human Vision and Electronic Imaging III, 3299, pp. 528-539, 1998.
Bellman, R. (1961). Adaptive Control Processes. Princeton University Press.
Joliffe, I. (1986). Principle Component Analysis. Springer-Verlag.
Durant Stephen C.
Kim Charles
Novak Druce & Quigg LLP
Proximex Corporation
Sherali Ishrat
LandOfFree
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