Content based Image Retrieval using Query based Feature Reduction with K-means Cluster Index

Content Based Image Retrieval is an active way of storing, searching, browsing, or retrieving images from a large image repository. Researchers are intensely competing for developing efficient and precise image retrieval tool which can be applicable to various image searching devices that runs on different platform. The proposed method develops an efficient Content Based Image Retrieval system by reducing number of features to obtain an optimal feature subset for the normalized feature set using query based feature selection. It also provides indexing for each cluster using k-means clustering. During the query phase, images are searched from the immediate neighboring cluster and also the next closest cluster in order to obtain the true positive images present in other nearby clusters. Gray Level Co-occurrence Matrix as texture features, Region based shape features and dominant color in RGB space are used for extracting the feature vector and Euclidean distance is used as dissimilarity measure. Performance can be compared among existing system by evaluating the retrieval precision and recall. The experiment is performed on Corel dataset and it shows competing performance with previous system.