Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
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Proceedings of the First International Workshop on Keyword Search on Structured Data
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CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
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Knowledge and Information Systems - Special Issue:Best Papers from the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2008);Guest Editors: Takashi Washio, Einoshin Suzuki and Kai Ming Ting
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ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
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Effective keyword search on image databases is a major open problem, due to the inherent imprecision of keywords (tags) used to describe images' content. In this paper we present a novel approach to deal with this problem, as implemented in the Scenique image retrieval and browsing system. Scenique is based on a multi-dimensional model, where each dimension is a tree-structured taxonomy of concepts, also called semantic tags, that are used to describe the content of images. We first describe an original algorithm, called Ostia (Optimal Semantic Tags for Image Annotation), that, by exploiting low-level visual features, tags, and metadata associated to an image, is able to predict a high-quality set of semantic tags for that image. Then, we describe how semantic tags can be effectively used for the purpose of improving the precision of keyword search.