CBIR Using Slant Transform Using DC & AC Coefficients

  • Authors

    • N Sravani
    • K Veera Swamy
    2018-07-04
    https://doi.org/10.14419/ijet.v7i3.6.15042
  • CBIR, slant transform, DC coefficients, AC coefficients, distance metrics, precision, recall.

  • In the CBIR- (Content Based Image Retrieval) technique requires low-level or primitive features- color, texture, or  other data that can be taken from its image Extracting feature vectors of database images as well as query image can be calculated with the help of slant transform by considering DC & 3 AC coefficients obtained in each block of an image. Slant transform represents the gradual brightness changes in an image line effectively. By calculating the difference between feature vector data base and feature vector for a query by using the distance measuring techniques. The vector of the smaller distance is the closest to query image. The experiment is performed in the Corel 500 Image Database. Finally, CBIR results are evaluated by the recall, precision, and F-Score.

     

     

  • References

    1. [1] Malik F & Baharudin B, “Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domainâ€, Journal of king saud university-computer and information sciences, Vol.25, No.2,(2013), pp.207-218.

      [2] Pratt W, Chen WH & Welch L, “Slant Transform Image Codingâ€, IEEE Transactions on Communications, (1974).

      [3] Jadhav SH & Ahmed SA, “Content based image retrieval system with hybrid feature set and recently retrieved image libraryâ€, International Journal of Computer Applications, Vol.59, No.5, (2012).

      [4] Mumtaz A, Gilani SAM & Jameel T, “A Novel Texture Image Retrieval System based on Dual Tree Complex Wavelet Transform and Support Vector Machinesâ€, IEEE 2nd International Conference on Emerging Technologies, (2006).

      [5] Soman S, Ghorpade M, Sonone V & Chavan S, “Content Based Image Retrieval using Advanced Color and Texture Featuresâ€, International Conference in Computational Intelligence (ICCIA), (2011).

      [6] Albuz E, Kocalar E & Khokhar AA, “Scalable color image indexing and retrieval using vector waveletsâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.13, No.5,(2001), pp.851-861.

      [7] Corel database

      http://wang.ist.psu.edu/~jwang/test1.tar

      [8] Malik FE & Baharudim B, “Effective content-based image retrieval: Combination of quantized histogram texture features in the DCT domainâ€, International Conference on Computer &Information Science (ICCIS), (2012).

      [9] Ruzon MA & Tomasi C, “Color Edge Detection witb tbeCompass Operatorâ€, IEEE conference on Computer Vision and Pattern Recognition, Vol.2, (1999), pp.160-166.

      [10] Komali A, Satish Kumar V, Ganapathi Babu K & Ratnam ASK, “3D Color Feature Extraction in Content-Based Image Retrievalâ€, International Journal of Soft Computing and Engineering (IJSCE), Vol.2, No.3, (2012).

      [11] Yumin T & Lixia M, “Image Retrieval Based on Multiple Features Using Waveletâ€, 5th IEEE International Conference on Computational Intelligence and Multimedia Applications, (2003).

  • Downloads

  • How to Cite

    Sravani, N., & Veera Swamy, K. (2018). CBIR Using Slant Transform Using DC & AC Coefficients. International Journal of Engineering & Technology, 7(3.6), 276-280. https://doi.org/10.14419/ijet.v7i3.6.15042