Application of Fuzzy Rule-Based Classifier to CBIR in comparison with other classifiers

Abstract— A great deal of research is being done in different aspects of Content-Based Image Retrieval (CBIR). Image classification is one of the most important tasks that must be dealt with in image DB as an intermediate stage prior to further image retrieval. The issue we address is an evolution from the simplest to more complicated classifiers. Firstly, there is the most intuitive one based on a comparison of the features of a classified object with a class pattern. Next, the paper presents decision trees and Naïve Bayes as another option in a great number of classifying methods. Lastly, to assign the most ambiguous objects we have built fuzzy rule-based classifiers. We propose how to find the ranges of membership functions for linguistic values for fuzzy rule-based classifiers according to crisp attributes. Experiments demonstrate the precision of each classifier for the crisp image data in our CBIR. Furthermore, these results are used to describe a spatial object location in the image and to construct a search engine taking into account data mining. The availability of image resources and large image datasets has increased dramatically. This has created a demand for effective and flexible techniques for automatic image classification and retrieval. Although attempts to construct the Content-Based Image Retrieval (CBIR) in an efficient way have been made before [3, 9, 11], a major problem in this area, i.e. the extraction of semantically rich metadata from computationally accessible low-level features, still poses a tremendous scientific challenge and constitutes a topic open to research. < final year projects >