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DOI: 10.14569/IJARAI.2016.051001
PDF

Image Retrieval Method Utilizing Texture Information Derived from Discrete Wavelet Transformation Together with Color Information

Author 1: Kohei Arai
Author 2: Cahya Ragmad

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 5 Issue 10, 2016.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Image retrieval method utilizing texture information which is derived from Discrete Wavelet Transformation: DWT together with color information is proposed. One of the specific features of the texture information extracted from portions of image is based on Dyadic wavelet transformation with forming texture feature vector by using energy derived from Gabor transform on 7 by 7 pixel neighbor of significant points. Using the Wang`s dataset, the proposed method is evaluated with retrieval success rate (precision and recall) as well as Euclidian distance between the image in concern (Query image) and the other images in the database of interest and is compared to the other method. As the result through the experiments, it is found that the DWT derived texture information is significantly effective in comparison to the color information.

Keywords: Wavelet; DWT; local feature; Color; Texture; CBIR

Kohei Arai and Cahya Ragmad, “Image Retrieval Method Utilizing Texture Information Derived from Discrete Wavelet Transformation Together with Color Information” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 5(10), 2016. http://dx.doi.org/10.14569/IJARAI.2016.051001

@article{Arai2016,
title = {Image Retrieval Method Utilizing Texture Information Derived from Discrete Wavelet Transformation Together with Color Information},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2016.051001},
url = {http://dx.doi.org/10.14569/IJARAI.2016.051001},
year = {2016},
publisher = {The Science and Information Organization},
volume = {5},
number = {10},
author = {Kohei Arai and Cahya Ragmad}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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