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This article presents an experimental evaluation on image representation using Latent Semantic Analysis (LSA) for searching very large image databases. Our aim is twofold: First, we experimentally investigate the structure and size of the feature space in order for LSA to bring efficient results. Second, we replace the Singular Value Decomposition (SVD) analysis on the feature matrix, by solving the eigenproblem of the term correlation matrix, a much less memory demanding task which significantly improved the performance in both accuracy and computational time (preprocessing and query response time) on three large image collections. Finally the new approach overcomes the high cost of updating the database after new insertions.