Unsupervised Methods on Image Database Using Cluster Mean Average Methods for Image Searching

Abstract

Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Image Retrieval systems are used in order to automatically index, search, retrieve, and surf image databases. Gathering 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 Algorithm for retrieving the images from huge volumes of data with better performance. This requires image processing methods like color histogram feature extraction, classification of images, retrieval, and indexing steps in order to develop an efficient image retrieval system. In this work, processing is done through the image clustering method which is used for feature extraction taken place, classification is done using K-means [1] classification algorithm [2]. For retrieval of images, Euclidian distance method values are calculated between query image and database images. The main aim of this work is to extract images with similarity when the images are retrieved based on query image.