Improving Semantic Concept Detection and Retrieval using Contextual Estimates

In this paper we introduce a novel contextual fusion method to improve the detection scores of semantic concepts in images and videos. Our method consists of three phases. For each individual concept, the prior probability of the concept is incorporated with detection score of an individual SVM dete...

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Bibliographic Details
Published in:	2007 IEEE International Conference on Multimedia and Expo pp. 536 - 539
Main Authors:	Aytar, Y., Orhan, O.B., Shah, M.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-07-2007
Subjects:	Computer science Couplings Detectors Graphical models Image retrieval Information retrieval Shape Support vector machine classification Support vector machines Videos
Online Access:	Get full text
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Summary:	In this paper we introduce a novel contextual fusion method to improve the detection scores of semantic concepts in images and videos. Our method consists of three phases. For each individual concept, the prior probability of the concept is incorporated with detection score of an individual SVM detector. Then probabilistic estimates of the target concept are computed using all of the individual SVM detectors. Finally, these estimates are linearly combined using weights learned from the training set. This procedure is applied to each target concept individually. We show significant improvements to our detection scores on the TRECVID 2005 development set and LSCOM-Lite annotation set. We achieved on average +3.9% improvements in 29 out of 39 concepts.
ISBN:	9781424410163 1424410169
ISSN:	1945-7871 1945-788X
DOI:	10.1109/ICME.2007.4284705