Similar image searching from image database using cluster mean sorting and performance estimation

Computer vision field over the last decades, Content-Based Image Retrieval (CBIR) systems are used in order to search, retrieve and browse image from databases. This accumulation of large collections of digital images has created the need for efficient and intelligent schemes for classifying and retrieval of images. In our proposed method, we are using, Clustering Algorithms for retrieving the images from huge volumes of data with better performance. This requires image processing, feature extraction, classification of images and retrieval steps in order to develop an efficient image retrieval system. In this work, processing is done through the image clustering method [1] which is used for feature extraction which is taken place. For retrieval of images, mean values are calculated between Query image and database images and all clustered mean values are considered as a sorted order. When the comparisons are allowed between the images, in our observation we founded excellent performance and similarities in between images. The main aim of this work is to extract images with good similarity when the images are retrieved based on query image.