Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: A System for Region-Based Image Indexing and Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Semantics-Based Image Retrieval by Region Saliency
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image retrieval by clustering
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Toward cross-language and cross-media image retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Image classification for content-based indexing
IEEE Transactions on Image Processing
Cross-Media database retrieval system based on TOTEM
WISE'06 Proceedings of the 7th international conference on Web Information Systems
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In this paper, we propose a novel method for cross-media semantic-based information retrieval, which combines classical text- based and content-based image retrieval techniques. This semantic-based approach aims at determining the strong relationships between keywords (in the caption) and types of visual features associated with its typical images. These relationships are then used to retrieve images from a textual query. In particular, the association keyword/visual feature may allow us to retrieve non-annotated but similar images to those retrieved by a classical textual query. It can also be used for automatic images annotation. Our experiments on two different databases show that this approach is promising for cross-media retrieval.