WordNet: a lexical database for English
Communications of the ACM
Experiments on using semantic distances between words in image caption retrieval
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
An Approach to Microscopic Clustering of Terms and Documents
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Overview of the ImageCLEF 2006 photographic retrieval and object annotation tasks
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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The use of visual features in text-based ad-hoc image retrieval is challenging. Visual and textual information have so far been treated equally in terms of their properties and combined with weighting mechanisms for balancing their contributions to the ranking. The use of visual and textual information in a single retrieval system sometimes limits its applicability due to the lack of modularity. In this paper, we propose an image retrieval method that separates the usage of visual information from that of textual information. Visual clustering establishes linkages between images and the relationships are later used for the reranking. By applying clustering on visual features prior to the ranking, the main retrieval process becomes purely text-based. Experimental results on the ImageCLEFphoto ad-hoc task show this scheme is suitable for querying multilingual collections on some search topics.