Image indexing using color histogram and k-means clustering for optimization CBIR in image database

Abstract

Retrieving visually similar images from image database needs high speed and accuracy. Various text and content based image retrieval techniques are being investigated by the researchers in order to exactly match the image features. In this paper, a content-based image retrieval system (CBIR), which computes color similarity among images, is presented. CBIR is a set of techniques for retrieving semantically relevant images from an image database based on automatically derived image features. Color is one important visual features of an image. This document gives a brief description of a system developed for retrieving images similar to a query image from a large set of distinct images with histogram color feature based on image index. Result from the histogram color feature extraction, then using K-Means clustering to produce the image index. Image index used to compare to the histogram color feature of query image and thus, the image database is sorted in decreasing order of similarity. The results obtained by the proposed system obviously confirm that partitioning of image objects helps in optimization retrieving of similar images from the database. The proposed CBIR method is compared with our previously existed methodologies and found better in the retrieval accuracy. The retrieval accuracy are comparatively good than previous works proposed in CBIR system.