— 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 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 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.
— Content based image retrieval, orthogonal polynomials, texture analysis, similarity measure, rotation and scale invariant.
R. Krishnamoorthi and S. Sathiya Devi are with the Anna University of Technology Trichirappalli, Tamilnadu, India (e-mail: email@example.com, firstname.lastname@example.org).
Cite: R. Krishnamoorthi and S. Sathiya Devi, " A Simple Computational Model for Texture Based Image Retrieval with Orthogonal Polynomials," International Journal of Innovation, Management and Technology vol. 4, no. 3, pp. 370-375, 2013.