Support vector machine with multiple kernel learning for image retrieval

Abstract

Content-based image retrieval (CBIR) is any technology that helps to organize digital image archives by their visual content, with the rapid growth of image amount, the presence of image retrieval is very helpful for people now. Basically image retrieval is part of classification problem. Support Vector Machine (SVM) is one of technique that can solve classification problem well. In fact, SVM is a linear classifier, it's mean that this algorithm can only be used to classify linear separable data. In order to classify not linear separable data, this algorithm should be modified with kernel learning. Determine the appropriate kernel during the learning process is difficult, therefore, many researchers are trying to develop more flexible kernel learning called Multiple Kernel Learning (MKL). This framework will build a classification model based on SVM algorithm modified with multiple kernel learning and applied to image retrieval. The image retrieval contain five categories of image and the experimental uses k-fold cross-validation. The result show that SVM with multiple kernel learning has good accuracy with 78 % and also has sort computation time, where it needs about 64.35 seconds for training session and 26.15 seconds for retrieve session.