A New Approach of Multilevel Unsupervised Clustering for Detecting Replication Level in Large Image Set
Sreedhar Kumar S, M. Gunashree, Syed Thouheed Ahmed, M. Sindhuja, P. Bhumika, B. Anusha, B G Ishwarya
Abstract
Replicate and near-replicate images are variants derived from original images that are common among the large number of images on the cloud, desktop and mobile storage areas respectively. The existence of such near-replicate an image in web search indicates the presence of redundancy and lowers the system performance. The detection of replication is challenging due to the multitude of possible variations of images. In this paper, a new system called Multilevel Clustering for Image Replication Identification (MCIRI) is presented. The proposed MCIRI system aims to identify the replication level of images over the large storage area based on multilevel unsupervised clustering techniques. The MCIRI consists of four stages: feature extraction, first level clustering, second level clustering and replication measurement. In the first stage, the MCIRI extract the three features over each individual image in the image set based on standard statistical operations such as mean, standard deviation and variance. Next stage, the MCIRI split the image feature vector of image-set into ‘K’ dissimilar groups based on K-means clustering technique. In the third stage, the MCIRI partially identifies the dissimilar cluster over each individual cluster in the cluster set of previous level clustering result. In the final stage, the MCIRI estimate the replication level of each individual cluster in the result of second level clustering based on the cluster validation measure. Experimental results show that the proposed MCIRI is better suitable to estimate the replication level in the image set.