SVM framework for incorporating content-based image retrieval and data mining into the SBIM image manager

Abstract

With the Internet evolution, there has been a huge increase in the amount of images stored in electronic format particularly in the case of biological and medical image applications. Nowadays, hospitals and research centers can acquire large image databases which poses fundamental requirements for storage, processing and sharing data. In order to fulfill these requirements we have proposed the Shared Biological Image Manager (SBIM) system which has been developed using the programming languages PHP and Javascript as well as the Database Management System PostgreSQL. In this chapter, we propose an extension of the SBIM functionalities by incorporating data mining and image retrieval facilities. We describe an unified solution for both these services inside the Shared Biological Image Manager (SBIM) through Support Vector Machine (SVM) frameworks. Data mining is implemented using discriminant weights given by SVM separating hyperplanes to select the most discriminant features for two-class classification problems. For image retrieval, we consider an SVM ensemble based on the “one-against-all” SVM multi-class approach. The user specifies an initial feature space, the training set and the SVM (ensemble) configuration. After training, the SVM ensemble can be used to retrieve relevant data once given a query image. Finally, we discuss some details about the implementation of the content-based image retrieval (CBIR) and discriminant features discovery approaches inside the SBIM system.