SHORT PAPER International Journal of Recent Trends in Engineering, Vol 2, No. 3, November 2009 108 Image Comparison Search Engine Based On Traditional and Improved Fractal Encoding Techniques. Shraddha Viraj Pandit 1 , M.V. Kulkarni 2 , M.L.Dhore 3 . 1 M.E. (CSE-IT) Student Department of Computer Engg.,Vishwakarma Institute of Technology,University of Pune, Pune,India svpandit_pict@yahoo.co.in, 2 Assistant Professor Department of Computer Engg,Vishwakarma Institute of Technology,University of Pune, Pune, India kul_mv@rediffmail.com, 3 Associate Professor and head of Department of Computer Engg, Vishwakarma Institute of Technology, University of Pune,Pune,India manikrao.dhore@vit.edu Abstract— This search engine allows users to quickly obtain information from networks. Traditional search engines can only search the data of modal characters. To solve this problem, Image Comparison Search Engine (ICSE) makes use of “Fractal Image processing “to create a database using image Eigen values. When a user input is an image query, this system will generate image Eigen value data, compare this with the data in the database of image Eigen value, and output the results. ICSE can not only find the exact input image for the source image, but also find the “right image” when the source image is rotated. Index Terms— fractal image compression, search engine, mean, range block, domain block I. INTRODUCTION Image search (or image search engine) is a type of search engine specialized on finding pictures, images, animations etc. With the rapid pace of computer technology over the past several years, the information that users use is no longer mainly character based. Traditional character based search engines are unable to provide the capabilities needed for searching image data. A search engine allows users to quickly obtain information from networks. Traditional search engines can only search the data of modal characters. The solution to this problem is to implement an Image Comparison Search Engine (ICSE), and make the use of “Fractal Image Processing” to create a database using image Eigen values. When user input an image query, this system will generate image Eigen value data, compare this with the data in the database of image Eigen value, and output the results. ICSE can not only find the exact input image for the source image, but also find the “right image” when the source image is rotated [2]. II. ICSE METHODOLOGY Here is the effort to design and implement a new search engine, called Image Comparison Search Engine (ICSE), to solve this problem. 1. The ICSE makes use of the comparison mode and returns correct or similar images from the database and processes the query without knowing the image filename. It is the image Eigen value database as a search engine kernel. 2. The ICSE try to reduce data space by only retrieving the Eigen value of the image by applying fractal image processing of the image in the spatial domain, and store the results in the image Eigen value database [2]. The entire ICSE process is broadly divided into four parts: 1. Image normalization 2. Retrieval of Eigen value from Fractal Image Processing 3. Image storage 4. Analysis (Eigen value comparison). The image Search Engine Work Flow Chart is shown in Fig.1. Fig.1. Image Search Engine Work Flow A. Image Normalization There are varieties of images on the Internet. Firstly there is a need to normalize the image properties of size, color Input Original Image Image Normalization Retrieve Eigen Value Image Storage Eigen value Comparison © 2009 ACADEMY PUBLISHER