A Machine Learning-Based Model for Content-Based Image Retrieval

Abstract

A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the k-Nearest Neighbors (k-NN) approach to retrieve databases. Due to some disadvantages of a such approach, the use of neighborhood graphs was proposed. This approach is interesting but it has some disadvantages consisting, mainly, in its complexity. This chapter presents a step in a long process of analyzing, structuring, and retrieving multimedia databases. Indeed, we propose an effective method for locally updating neighborhood graphs which constitute our multimedia index. Then, we exploit this structure in order to make easy and effective the retrieval process using queries in an image form in one hand. In another hand, we use the indexing structure to annotate images in order to describe their semantics. The proposed approach is based on an intelligent manner for locating points in a multidimensional space. Promising results are obtained after experimentations on various databases. Future issues of the proposed approach are very relevant in this domain.