English

Abstract

Content-based image retrieval (CBIR) is a technique which uses visual content to search and compare images from large scale image databases according to the interests of users. Visual content of the image like color, shape, texture and spatial layout are widely used in CBIR. Texture features has been successfully used to provide a meaningful tool for searching image databases, as texture images generally contain unique visual patterns or spatial arrangements of pixels, so that describing textures, gray- level or color alone, may not yield to classify similar ones. In this paper we mainly concentrate on Spectral methods which extract texture features from the energy distribution in the frequency domain. The most popular spectral feature extraction methods are Fourier, wavelet, and Gabor filtering. In the first process it will extract the features from query and database images to a distinguishable extent using Gabor and wavelet Transforms. In the second process it involves matching these features to yield a result that is visually similar by feature matching process.