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Context-based conceptual image indexing

Abstract : Automatic semantic classification of image databases is very useful for users searching and browsing, but it is at the same time a very challenging research problem as well. Local features based image classification is one of the key issues to bridge the semantic gap in order to detect concepts. This paper proposes a framework for incorporating contextual information into the concept detection process. The proposed method combines local and global classifiers with stacking, using SVM.We studied the impact of topologic and semantic contexts in concept detection performance and proposed solutions to handle the large amount of dimensions involved in classified data. We conducted experiments on TRECVID�04 subset with 48104 images and 5 concepts. We found that the use of context yields a significant improvement both for the topologic and semantic contexts.
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https://hal.inria.fr/hal-00953903
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Stéphane Ayache, Georges Quénot, Shin'Ichi Satoh. Context-based conceptual image indexing. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006, Toulouse. ⟨hal-00953903⟩

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