An application of one-class support vector machines in content-based image retrieval

Fast and accurate image classification is becoming one of the key requirements in content-based image retrieval (CBIR). Various methods including artificial neural networks have been used to classify a large image database efficiently and shown to be highly successful in this application area. This paper presents a one-class support vector machine (SVM) based classification method that can categorize a large image database efficiently by color and text content for content-based image retrieval. In order to evaluate one-class SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real real-world image data. The experiment shows that the results of one-class SVMs outperform those of ANNs.