An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM

Retrieval of images containing multiple objects has been an active research area in recent years. This paper presents an efficient retrieval system for images containing multiple objects using shape, texture, edge histogram features and SVM (Support Vector Machine). The process starts with preparing the knowledge database. In this, all the images in dataset are segmented and shape, texture, edge histogram features are extracted. A combined feature vector is generated by combining these multiple features. These features are trained by using SVM and all the images in database are classified into different classes. This information is stored as knowledge data base. When the user enters his choice of query image, it is segmented and features are extracted. The combined feature vector is generated by using all these features. SVM is used for matching of the query image feature vector class with the classes in knowledge database. The similarity distance is calculated between the feature vector of the query image and the images in the corresponding class. The images are sorted and displayed according to the ascending order of the similarity distance. Experimental results demonstrate better retrieval efficiency.