Texture image retrieval using rotated wavelet filters Manesh Kokare * , P.K. Biswas, B.N. Chatterji Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur 721 302 (WB), India Received 3 January 2005; received in revised form 10 August 2006 Available online 21 February 2007 Communicated by R. Manmatha Abstract A novel approach for texture image retrieval is proposed by using a new set of two-dimensional (2-D) rotated wavelet filters (RWF) and discrete wavelet transform (DWT) jointly. A new set of 2-D rotated wavelet improves characterization of diagonally oriented tex- tures. Experimental results indicate that the proposed method improves retrieval rate from 70.09% to 78.44% on database D1, and from 75.62% to 80.78% on database D2, compared with the traditional DWT based approach. The proposed method also retains comparable levels of computational complexity. Ó 2007 Elsevier B.V. All rights reserved. Keywords: Content based image retrieval; Rotated wavelet filters; Canberra distance metric; Similarity measurement; Texture retrieval; Wavelets 1. Introduction 1.1. Motivation With the rapid expansion of worldwide network and advances in information technology there is an explosive growth of multimedia databases and digital libraries. This demands an effective tool that allow users to search and browse efficiently through such a large collections. In many areas of commerce, government, academia, hospitals, entertainment, and crime preventions large collections of digital images are being created. Usually, the only way of searching these collections was by using keyword indexing, or simply by browsing. However, as the databases grew lar- ger, people realized that the traditional keywords based methods to retrieve a particular image in such a large col- lection are inefficient. To describe the images with key- words with a satisfying degree of concreteness and detail, we need a very large and sophisticated keyword system containing typically several hundreds of different key- words. One of the serious drawbacks of this approach is the need of trained personnel not only to attach keywords to each image (which may take several minutes for one sin- gle image) but also to retrieve images by selecting key- words, as we usually need to know all keywords to choose good ones. Further, such a keyword based approach is mostly influenced by subjective decision about image content and also it is very difficult to change a key- word based system afterwards. Therefore, new techniques are needed to overcome these limitations. Digital image databases however, open the way to content based search- ing. It is common phrase that an image speaks thousands of words. So instead of manual annotation by text based keywords, images should be indexed by their own visual contents, such as color, texture and shape. The main advantage of this method is its ability to support the visual queries. Hence researchers turned attention to content based image retrieval (CBIR) methods. The challenge in image retrieval is to find out features that capture the important characteristics of an image, which make it 0167-8655/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.patrec.2007.02.006 * Corresponding author. Address: Department of Electronics and Telecommunication Engineering, S.G.G.S. Institute of Engineering and Technology, Vishnupuri, Nanded 431 602 (Maharashtra), India. Tel.: +91 2462 268699 (residence); +91 2462 229306x220 (office); fax: +91 2462 229236. E-mail addresses: mbk@ece.iitkgp.ernet.in, mbkokare@sggs.ac.in, mbkokare@yahoo.com (M. Kokare), pkb@ece.iitkgp.ernet.in (P.K. Bis- was), bnc@ece.iitkgp.ernet.in (B.N. Chatterji). www.elsevier.com/locate/patrec Pattern Recognition Letters 28 (2007) 1240–1249