An efficient method for image mining using GLCM and neural network

  • Authors

    • T R. Nisha Dayana
    • Dr A. Lenin Fred
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.13859
  • GLCM (Gray Level Co-Occurrence Matrix) Feature, CBIR (Content Based Image Retrieval), ANN (Artificial Neural Network), ABC (Artificial Bee Colony).
  • Abstract

    Currently, content-based Image recovery (CBIR) drives for producing approaches which supports viable searching and scanning of vast picture progressive libraries by considering unwavering image texture features and has been a rapidly growing inspection bearing among image information recovery, computer vision, and database. The learning procedure of CBIR is achieved with the Neural Network method together with GLCM feature abstraction in our projected technique. Furthermore, with the ABC algorithm the normal/abnormal arrangement of the medical dataset images is managed. Lastly, to regulate the function of the projected method the solutions were replicated and associated with the available method. In the working platform of MATLAB, the projected method is applied.

     

     
  • References

    1. [1] N. Acosta-Mendoza, A. Gago-Alonso & J. E. Medina-Pagola, Frequent approximate subgraphs as features for graph-based image classification, Knowledge-Based Systems, 27 (2012), 381-392.

      [2] L. Gueguen & G. K. Ouzounis, Hierarchical data representation structures for interactive image information mining, International Journal of Image and Data Fusion, 3 (2012), 221-241.

      [3] M. Sahu, M. Shrivastava, & M. A. Rizvi, Image mining: a new approach for data mining based on texture, In proceedings of: Third International Conference on Computer and Communication Technology (ICCCT), IEEE, (2012), 7-9.

      [4] B. Minaei-Bidgoli, R. Barmaki & M. Nasiri, Mining numerical association rules via multi-objective genetic algorithms, Information Sciences, 233 (2013), 15-24.

      [5] Y. Wang, G. Wu, G. S. Chen & T. Chai, Data mining based noise diagnosis and fuzzy filter design for image processing, Computers & Electrical Engineering, 40 (2014), 2038-2049.

      [6] J. Deshmukh, & U. Bhosle, Image Mining Using Association Rule for Medical Image Dataset, Procedia Computer Science, 85 (2016), 117-124.

      [7] C. Singh & K. P. Kaur, A fast and efficient image retrieval system based on color and texture features, Journal of Visual Communication and Image Representation, 41 (2016), 225-238.

      [8] G. H. Liu, J. Y. Yang & Z. Li, Content-based image retrieval using computational visual attention model, pattern recognition, 48 (2015), 2554-2566.

      [9] J. Jayanth, S. Koliwad & T. A. Kumar, Classification of remote sensed data using Artificial Bee Colony algorithm, The Egyptian Journal of Remote Sensing and Space Science, 18 (2015), 119-126.

  • Downloads

  • How to Cite

    R. Nisha Dayana, T., & A. Lenin Fred, D. (2018). An efficient method for image mining using GLCM and neural network. International Journal of Engineering & Technology, 7(2.33), 76-83. https://doi.org/10.14419/ijet.v7i2.33.13859

    Received date: 2018-06-08

    Accepted date: 2018-06-08

    Published date: 2018-06-08