||In recent years, the expansion of acquisition devices such as digital cameras, the development of storage and transmission techniques and the success of tablet computers facilitate the development of many large image databases as well as the interactions with the users. This thesis  deals with the problem of Content-Based Image Retrieval (CBIR) on these huge masses of data. Traditional CBIR systems generally rely on three phases: feature extraction, feature space structuring and retrieval. In this thesis, we are particularly interested in the structuring phase (normally called indexing phase), which plays a very important role in finding information in large databases. This phase aims at organizing the visual feature descriptors of all images into an efficient data structure in order to facilitate, accelerate and improve further retrieval. We assume that the feature extraction phase is completed and the image feature descriptors which are usually low-level features describing the color, shape, texture, etc. of all images are available. Instead of traditional structuring methods, clustering methods which organize image descriptors into groups of similar objects (clusters), without any constraint on the cluster size, are studied. The aim is to obtain an indexed structure more adapted to the retrieval of high dimensional and unbalanced data. Clustering process can be done without prior knowledge (unsupervised clustering) or with a limited amount of prior knowledge (semi-supervised clustering).
||Advisors: Muriel Visani, Alain Boucher, Jean-Marc Ogier. Date and location of PhD thesis defense: 2 October 2013, University of La Rochelle
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||other ; abstract ; publishedVersion
Semi-supervised clustering ;
Interactive learning ;
Image indexing ;
Classification and clustering
||ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 2 (2014) , p. 45-46, ISSN 1577-5097