Pattern Mining and Clustering on Image Databases

Pattern Mining and Clustering on Image Databases

Marinette Bouet (LIMOS, Blaise Pascal University-Clermont-Ferrand, France), Pierre Gançarski (LSIIT-AFD-Louis Pasteur University, France Marie-Aude Aufaure Supélec—INRIA, France) and Omar Boussaïd (University LUMIERE Lyon, France)
DOI: 10.4018/978-1-59904-951-9.ch018
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Abstract

Analysing and mining image data to derive potentially useful information is a very challenging task. Image mining concerns the extraction of implicit knowledge, image data relationships, associations between image data and other data or patterns not explicitly stored in the images. Another crucial task is to organize the large image volumes to extract relevant information. In fact, decision support systems are evolving to store and analyse these complex data. This paper presents a survey of the relevant research related to image data processing. We present data warehouse advances that organize large volumes of data linked with images and then, we focus on two techniques largely used in image mining. We present clustering methods applied to image analysis and we introduce the new research direction concerning pattern mining from large collections of images. While considerable advances have been made in image clustering, there is little research dealing with image frequent pattern mining. We shall try to understand why.

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