Technical Program

Paper Detail

Paper:	IMDSP-P4.12
Session:	Image/Video Indexing and Retrieval
Time:	Tuesday, May 16, 16:30 - 18:30
Presentation:	Poster
Topic:	Image and Multidimensional Signal Processing: Image Indexing and Retrieval
Title:	CONTEXT-BASED CONCEPTUAL IMAGE INDEXING
Authors:	Stéphane Ayache, Georges Quénot, CLIPS / IMAG, France; Shin'ichi Satoh, National Institute of Informatics, Japan
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. 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.