Machine vision
The nature of statistical learning theory
The nature of statistical learning theory
Spatial Color Indexing and Applications
International Journal of Computer Vision
"What is in that video anyway?": In Search of Better Browsing
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
Using dual cascading learning frameworks for image indexing
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
Active selection for multi-example querying by content
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Combining intra-image and inter-class semantics for consumer image retrieval
Pattern Recognition
A hybrid framework for detecting the semantics of concepts and context
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Fusing concept detection and geo context for visual search
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Extraction of semantic features from visual concepts is essential for meaningful content management in terms of filtering, searching and retrieval. Recently, machine learning techniques have been shown to provide a computational framework to map low level features to high level semantics. In this paper we expose these techniques to the challenge of supporting a moderately large lexicon of semantic concepts. Using the TREC 2002 benchmark corpus for training and validation we investigate a support vector machine based learning system for modeling 34 visual concepts. The detection results show excellent performance for a set of concepts with moderately large training samples. Promising performance is also observed for concepts with few training concepts.