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Book Sections Year : 2007

Pattern Mining and Clustering on Image Databases

Marinette Bouet
Pierre Gankarski
  • Function : Author

Abstract

Nowadays, organizations are dealing with multimedia data integrating different formats like images, audios, videos, texts, graphics or XML documents. In this paper, we focus our presentation on the media image. Analyse and mine these multimedia data to derive potentially useful information is a very challenging task. For example, image mining deals with 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 these large volumes of image data in order to extract relevant information. In fact, decision support systems (DSS) such as data warehousing, data mining or on-line analytical processing (OLAP) are evolving to store and analyse this kind of complex data. This paper presents a survey of relevant works 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. On one hand, we present clustering methods applied to image analysis, and on the other hand, we introduce the new research direction concerning pattern mining from large collection of images. Since we have overviewed some of the recent related works, we notice that a lot of advances are made in image clustering with very significant results. We also note that there is few researches works dealing with image frequent pattern mining and try to understand why.
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Dates and versions

hal-00256568 , version 1 (15-02-2008)

Identifiers

  • HAL Id : hal-00256568 , version 1

Cite

Marinette Bouet, Pierre Gankarski, Marie-Aude Aufaure, Omar Boussaid. Pattern Mining and Clustering on Image Databases. Successes and New Directions in Data Mining, Idea Group Publishing, pp.187-212, 2007. ⟨hal-00256568⟩
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