Computer Vision Based Content-Based Image Classification System

  • Malay S. Bhatt
  • Tejas Patalia
Keywords: Classification, Edge Detection, Confidence Co-Occurrence Matrix, Histogram, Local Binary Pattern, Support Vector Machine.


In this paper, computer vision based Content-Based Image Classification systems have been described which are useful in various service and product industries. We have proposed Confidence Co-occurrence Matrix, which is a modification of Generalized Co-occurrence Matrix. The proposed framework merges properties of Confidence Co-occurrence Matrix along with other features such as RGB and HSV Histograms, Local Binary Pattern and Canny’s edge detection approach. Proposed approach creates a fixed- size descriptor of size 1632. Once a feature vector has been constructed, classification is performed using Linear Support Vector Machine. The System is tested on four different wellknown datasets namely, sport events Database, Flavia Leaf Dataset, Leeds Butterfly Dataset and Birds Dataset . The proposed system is implemented in MATLAB and achieves an average class accuracy of 96%, 99%,95% and 95% for the four datasets respectively


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How to Cite
Bhatt, M. S., & Patalia, T. (2019). Computer Vision Based Content-Based Image Classification System. Statistics, Optimization & Information Computing, 7(4), 840-853.
Research Articles