Abstract
In this paper, we investigate the intensive use of Correspondence Analysis (CA) for large scale content-based image retrieval. Correspondence Analysis is a useful method for analyzing textual data and we adapt it to images using the SIFT local descriptors. CA is used to reduce dimensions and to limit the number of images to be considered during the search step. An incremental algorithm for CA is proposed to deal with large databases giving exactly the same result as the standard algorithm. We also integrate the Contextual Dissimilarity Measure in our search scheme in order to improve response time and accuracy. We explore this integration in two ways: (i) off-line (the structure of image neighborhoods is corrected off-line) and (ii) on-the-fly (the structure of image neighborhoods is adapted during the search). The evaluation tests have been performed on a large image database (up to 1 million images).
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Pham, NK., Morin, A., Gros, P., Le, QT. (2010). Intensive Use of Correspondence Analysis for Large Scale Content-Based Image Retrieval. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00580-0_4
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