Image Similarity Matching Based On Colour and Texture.pdf

1、Retrieving images from large and varied collectionsusing content as a key is a challenging and importantproblem. The purpose of this paper is tfind out thesolution to the problem of designig a Content Based

2、Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes as well as its vast deployment in various applications, the need for CBIR development has increased. This

3、 paper outlines a description of the primitive features of an image such as colour and texture. These features are extracted and used as the basis for a similarity check between images. This pa

4、per describes how to extract the primitive features of a query image and compare them to those of database images. The first step in the process is extracting image features t

5、o a distinguishable extent. And the second step involves matching these features to yield a result that is visually similar. To extract image using colour feature I propose colour histogra

6、m method. First, we supply a query image and we compare histogram quadratic distances between query image and the images in a database, then we retrieve images with least distances. In this resea

7、rch I propose Gabor filters to extract texture feature from an image. Image retrieval using texture similarity may not be very useful. But image retrieval using texture similarity can often be useful in di

8、stinguishing areas of image with similar colour such as sky and sea, or leaves and grass. Here, texture queries can be formulated in a similar manner to colour queries, by supplying an examp

9、le query image. The system then retrieves images with texture measures most similar in value to the query. The paper is organized as follows: Chapter 1,Introduction. Chapter 2, Related