Techniques for video retrieval based on HMM similarity

Details Techniques for video retrieval based on HMM similarity Techniques for video retrieval based on HMM similarity

2007-12-11

2007-12-11

Fleurantin, Jean Bolte (Department: 2162)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000, C707S793000, C704S001000, C704S256100, C382S220000

Reexamination Certificate

active

11021717

ABSTRACT:
A query is received. The query may be an object containing temporal information. A query model including static and temporal components is then determined for the object. A weighting for static and temporal components is also determined. The query model is then compared with one or more search models. The search models also include static and temporal components. Search results are then determined based on the comparison. In one embodiment, the comparison may compare the static and temporal components of the query model and the search model. A weighting of the differences between the static and temporal components may be used to determine the ranking for the search results.

REFERENCES:
patent: 2002/0150300 (2002-10-01), Lee et al.
Hiroshi Matsumoto et al., A Frequency-weighted HMM Based on Minimum Error Classification for Noise Speech Recognition, Apr. 21-24, 1997, IEEE, 1511-1514.
Stefan Eickeler, Face Database Retrieval Using Pseudo 2D Hidden Markov Models, May 20-21, 2002, IEEE, 58-63.
Bahlmann, Claus et al.; “Measuring HMM Similarity with the Bayes Probability of Error and its Application to Online Handwriting Recognition”; 2001,Proceedings of the ICDAR, pp. 406-4111.
DeMenthon, Daniel et al.; “Relevance Ranking of Video Data Using Hidden Markov Model Distances and Polygon Simplification”; 2000,VisualLNCS 1929, pp. 49-61.
Dimitrova, N. et al.;“Content-based Video Retrival by Example Video Clip”; 1997,SPIE,vol. 3022, pp. 59-71.
Do, M.N.; “Fast Approximation of Kullback-Leibler Distance Trees and Hidden Markov Models”; 2003,IEEE Signal Processing Letters,vol. 10, No. 4, pp. 115-118.
Eickeler, S. et al.; “Content-Based Video Indexing of TV Broadcast News Using Hidden Markov Models”; 1999,Proceedings of the ICASSP, pp. 2997-3000.
Falkhausen, M. et al.; “Calculation of Distance Measures between Hidden Markov Models”; 1995,Proceedings of EuroSpeech, pp. 1487-1490.
Flickner, Myron et al.;“Query by Image and Video Content: The QBIC System”; 1995,Computer, vol. 28, No. 9, pp. 23-32.
Juang B.H. et al.; “A Probabilistic Distance Measure for Hidden Markov Models”; 1985,AT&T Technical Journal,vol. 62, No. 2, pp. 391-408.
Lienhart, R.; “Systematic Method to Compare and Retrieval Video Sequences”; 1998,SPIE, vol. 3312, pp. 271-282.
Lyngso, R.B. et al.; “Metrics and similarity measures for hidden Markov models”; 1999,Proceedings of Intelligent System for Molecular Biology, pp. 178-186.
Sebe, N. et al.; “Computer Vision and Image Understanding”; 2003,Computer Vision and Image Understanding,vol. 92, No. 2-3, pp. 141-146.
Veltkamp, Remco C. et al.; “A Survey of Content-Based Image Retrieval Systems”; 2002,Content-Based Image and Video Retrieval,pp. 47-101.
Vihola, M. et al.; “Two Dissimilarity Measures for HMMs and their Application in Phoneme Model Clustering”; 2002,Proceedings of the ICASSP,pp. 933-936.
“Mar. 8, 2004 VISMeister goes on sale”; http://www.ricoh.com/src
ews/04—01-06.html, 1 page.

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