Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model

Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model

R. Krishnamoorthi (Anna University of Technology, India) and S. Sathiya Devi (Anna University of Technology, India)
Copyright: © 2011 |Pages: 23
DOI: 10.4018/ijcvip.2011100103


The exponential growth of digital image data has created a great demand for effective and efficient scheme and tools for browsing, indexing and retrieving images from a collection of large image databases. To address such a demand, this paper proposes a new content based image retrieval technique with orthogonal polynomials model. The proposed model extracts texture features that represent the dominant directions, gray level variations and frequency spectrum of the image under analysis and the resultant texture feature vector becomes rotation and scale invariant. A new distance measure in the frequency domain called Deansat is proposed as a similarity measure that uses the proposed feature vector for efficient image retrieval. The efficiency of the proposed retrieval technique is experimented with the standard Brodatz, USC-SIPI and VisTex databases and is compared with Discrete Cosine Transform (DCT), Tree Structured Wavelet Transform (TWT) and Gabor filter based retrieval schemes. The experimental results reveal that the proposed method outperforms well with less computational cost.
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1. Introduction

With the rapid growth of digital and information technologies, more and more multimedia data are generated and made available in digital form. Searching and retrieving relevant images in this huge volume of data is a difficult task and has created an urgent need to develop new tools and techniques. One such solution is the Content Based Image Retrieval (CBIR). As the image databases grow larger, the traditional keyword-based approach for retrieving a particular image becomes inefficient and suffers from the following limitations (i) Vast amount of labor is required for manual image annotation and (ii) Limited capacity for retrieving the visual content of the image and subjectivity of human perception. Hence, to overcome these difficulties of manual annotation approach, content based image retrieval has emerged. CBIR is a collection of techniques and algorithms which enable querying the image databases with low level image content such as color, texture, objects and their geometries rather than textual attributes such as image name or other keywords (Rangachar Kasturi & Jain, 2002). Many image retrieval systems have been developed using all or some of these features. It includes Chabot (Ogle & Stonebraker, 1995), Photobook (Pentland, Picard, & Sclaroff, 1996), QBIC (Flickner et al., 1995), Virage (Batch et al., 1996), VisualSeek (Smith & Chang, 1997), MARS (Huang, Mehrotra, & Ramachandran, 1996), Netra (Ma & Manjunath, 1995), and Excalibur (Feder, 1997). The extensive literature and the state of art methods about content based image retrieval can be found in Datta, Li, and Wang (2005), Rui, Huang, and Chang (1999), Smeulders, Worring, Santini, Gupta, and Jain (2000), Kherfi, Ziou, and Bernardi (2004), Lew, Sebe, Djeraba, and Jain (2006), and Kokare, Chatterji, and Biswas (2002).

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