Welcome to the InfoSci Platform
Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model

Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model

R. Krishnamoorthi, S. Sathiya Devi
Copyright: © 2013 |Pages: 23
ISBN13: 9781466639065|ISBN10: 1466639067|EISBN13: 9781466639072
DOI: 10.4018/978-1-4666-3906-5.ch017
Cite Chapter Cite Chapter

MLA

Krishnamoorthi, R., and S. Sathiya Devi. "Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model." Intelligent Computer Vision and Image Processing: Innovation, Application, and Design, edited by Muhammad Sarfraz, IGI Global, 2013, pp. 239-261. https://doi.org/10.4018/978-1-4666-3906-5.ch017

APA

Krishnamoorthi, R. & Devi, S. S. (2013). Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model. In M. Sarfraz (Ed.), Intelligent Computer Vision and Image Processing: Innovation, Application, and Design (pp. 239-261). IGI Global. https://doi.org/10.4018/978-1-4666-3906-5.ch017

Chicago

Krishnamoorthi, R., and S. Sathiya Devi. "Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model." In Intelligent Computer Vision and Image Processing: Innovation, Application, and Design, edited by Muhammad Sarfraz, 239-261. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3906-5.ch017

Export Reference

Mendeley
Favorite

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.