International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number 9–Nov2014 ISSN: 2231-5381 http://www.ijettjournal.org Page 429 Rotation Invariant Content-Based Image Retrieval System P. Vijaya Bharati 1 , A.Rama Krishna 2 1,2 Assistant Professor Department of Computer Science & Engineering, Vignan’s Institute of Engineering for Women, AP, India Abstract—The emergence of multimedia technology and the rapid growth in the number and type of multimedia assets controlled by several entities, yet because the increasing range of image and video documents showing on the Internet, have attracted vital analysis efforts in providing tools for effective retrieval and management of visual data. So the need for image retrieval system arose. Out of many existing systems “ROTATION INVARIANT CONTENT-BASED IMAGE RETRIEVAL SYSTEM” is the most efficient and accurate one. Effective texture feature is an essential component in any CBIR system. In the past, spectral features like Gabor and Wavelet have shown superior retrieval performance than most statistical and structural options. Recent researches on multi-resolution analysis have found that curvelet captures texture properties like curves, lines and edges with additional accuracy than Gabor filters. However, the texture feature extracted using curvelet transform is not rotation invariant. This can degrade its retrieval performance considerably, particularly in cases where there are many similar images with different orientations. We analyses the curvelet transform and derives a useful approach to extract rotation invariant curvelet features. The new system which uses curvelet transform for extracting texture features includes rotation invariant. Keywords: Texture features, Color features, Shape features, Rotation Invariant, Gabor Filters, Wavelets I. INTRODUCTION Databases of art works, satellite and medical imagery have been attracting more and more users in various professional fields — for example, medicine, geography, architecture, advertising, design, fashion, and publishing. Effectively and efficiently retrieving relevant images from large and varied image databases is now a necessity. Many Retrieval systems are developed. All are based on either text (image_name) or content of an image [7]. In the Text-Based Image Retrieval System, we can retrieve images supported by keywords i.e. we give an image_name as input and based on this name, images having similar names are retrieved. For example, suppose if we want to search all images named Roses from a large database, we give an input as Rose.jpg. But if the database also contains other images (not roses) having the same name as Rose.jpg, then we can also get those images which are irrelevant for our search. To improve the efficiency of these existing systems, Content-Based Image Retrieval Systems are developed. In the Content-Based Image Retrieval System, we can retrieve images based on content of an image i.e. Texture, Shape, Color features [1]. We have to build a database consisting the features of all. Fig. 1.1 Representation of a Digital Image. The notation that is used to represent the complete M*N digital image in a matrix form as shown in Figure 1.2. Fig. 1.2 Matrix Representation of a Digital Image 1.1.2 Digital Image processing The field of digital image processing refers to process digital images by means of a computer. A digital image is consists of a finite variety of components. Every component has a particular location and value. These components are cited as picture elements, image elements, pels, and pixels