Context-Based Conceptual Image Indexing

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. Local features based image classification is one of the promising way to bridge the semantic gap in detecting concepts. This paper proposes a framework for incorporating contextual information into the concept detection process. The proposed method combines local and global classifiers (SVMs) with stacking. 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 data set with 48104 images and 5 concepts. We found that the use of context yields a significant improvement both for the topologic and semantic contexts