Impact of image feature selection on retrieval accuracy

  • Authors

    • Sumaira Muhammad Hayat Khan
    • Ayyaz Hussain
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.15478
  • Content Based Image Retrieval, Feature Extraction, Feature Vector, Similarity Measure.
  • Two main approaches for retrieving a required image from a database are known as the local approach and the global approach. Each method is named based on the type of feature extraction technique used and the calculation of the feature vector. This paper proposes content based image retrieval technique at local level incorporating all the rudimentary features. Retrieval results of the proposed method are compared to find the optimal approach to image retrieval. The proposed system is also evaluated by comparing their experimental results with some state of the art system in the literature.

     

     


     
  • References

    1. [1] Sumaira Muhammad Hayat Khan and Ayyaz Hussain, “A hybrid approach to content based image retrieval using computational intelligence techniquesâ€, Indian Journal of Science and Technology, June 2016, 9(21), pp 1 – 8.

      [2] W.Y. Ma and B.S. Manjunath, “NeTra: a toolbox for navigating large image databases,†Multimedia Systems, vol. 7, pp. 184–198, 1999.

      [3] T. Deselaers, “Image retrieval, object recognition, and discriminative models,†Ph.D. dissertation, RWTH Aachen University, Aachen, Germany, 2008.

      [4] Sumaira Muhammad Hayat Khan, Ayyaz Hussain and Imad Fakhri Taha Alshaikhli, “An adequate approach to image retrieval based on local level feature extractionâ€, Mehran University Research Journal of Engineering & Technology, Oct 2015, 34(4), pp 337 – 349.

      [5] G. Pass and R. Zabith, “Histogram refinement for content-based image retrieval,†in IEEE Workshop on Applications of Computer Vision, 1996, pp. 96–102.

      [6] J. Huang et al., “Image indexing using color correlogram,†in IEEE Int. Conf. Computer Vision and Pattern Recognition, June 1997, pp. 762–768.

      [7] A. Rao et al., “Spatial color histograms for content-based image retrieval,†in IEEE Int. Conf. Tools with Artificial Intelligence, 1999, pp. 183–186.

      [8] L. Cinque et al., “Color-based image retrieval using spatial-chromatic histogram,†Image and Vision Computing, pp. 979–986, 2001.

      [9] Faloutsos et al., “Efficient and effective querying by image content,†Journal of Intelligent Information Systems, vol. 3, pp. 231- 262, July 1994.

      [10] ] M. Flickner et al., “Query by image and video conten: the QBIC systemâ€, IEEE 11th annual computer security applications conference, 1995, pp.23 – 32.

      [11] Pentland et al., “Photobook: Content-based manipulation of image databases,†International Journal of Computer Vision, vol. 18, no. 3, pp. 233-254, 1996.

      [12] Carson et al., “Blobworld: Image segmentation using expectation maximization and its application to image querying,†IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1026-1038, Aug. 2002.

      [13] J. R. Smith and S. Fu Chang, “Tools and techniques for color image retrieval,†Storage and Retrieval for Image and Video Databases, vol. 2670, 1996.

      [14] S. Nandagopalan et al., “A universal model for content based image retrieval,†International Journal of Computer Science, vol. 4, no. 4, pp. 242, 2009.

      [15] S. M. Khan et al., “Comparative study on content-based image retrieval (CBIR),†in Int. Conf. on Advanced Computer Science Applications and Technologies (ACSAT), Nov 2012, pp. 61–66.

  • Downloads

  • How to Cite

    Muhammad Hayat Khan, S., & Hussain, A. (2018). Impact of image feature selection on retrieval accuracy. International Journal of Engineering & Technology, 7(2.33), 691-694. https://doi.org/10.14419/ijet.v7i2.33.15478