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User-supplied data such as browsing logs, click-through data, and relevance feedback judgements are an important source of knowledge during semantic indexing of documents such as images and video. Low-level indexing and abstraction methods are limited in the manner with which semantic data can be dealt. In this paper and in the context of this semantic data, we apply latent semantic analysis on two forms of usersupplied data, real-world and artificially generated relevance feedback judgementsdoi:10.1109/cbmi.2008.4564964 dblp:conf/cbmi/MorrisonMB08 fatcat:cn5l7qyh5vaojeeyskzhqrjpu4