TAILIEUCHUNG - Multimedia_Data_Mining_06

Chapter 6 (MDM) | Part III Multimedia Data Mining Application Examples 179 © 2009 by Taylor & Francis Group, LLC Chapter 5 Image Database Modeling – Semantic Repository Training Introduction This chapter serves as an example to investigate content based image database mining and retrieval, focusing on developing a classification-oriented method- ology to address semantics-intensive image retrieval. In this specific approach, with Self Organization Map (SOM) based image feature grouping, a visual dic- tionary is created for color, texture, and shape feature attributes, respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space, we define a structure, called an α-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the image database. With the α-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncer- tainty existing and overlapping among the repositories in the feature space. An algorithm using classification accuracy measures is developed to combine the built classification tree with the fuzzy set modeling method to deliver se- mantically relevant image retrieval for a given query image. The experimental evaluations have demonstrated that the proposed approach models the seman- tic relationships effectively and outperforms a state-of-the-art content based image mining system in the literature in both effectiveness and efficiency. The rest of the chapter is organized as follows. Section introduces the background of developing this semantic repository training approach to image classification. briefly describes the previous work. In Section , we present the image feature extraction method as well as the creation of visual dictionaries for each feature attribute. In Section we introduce the concept of the α-semantics graph and show how to model the .

• Cơ sở dữ liệu
• Multimedia Data Mining (Ch 6)