Flower image classification with basket of features and multi layered artificial neural networks

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Artificial intelligence is penetrating most of the classification and recognition tasks performed by a computer. This work proposes to classify flower images based on features extracted during segmentation and after segmentation using multiple layered neural networks. The segmentation models used are watershed, wavelet, wavelet fusion model, supervised active contours based on shape, color and Local binary pattern textures and color, fused textures based active contours. Multi-dimension feature vectors are constructed from these segmented results for each indexed flower image labelled with their name. Each feature becomes input to a neuron in various feature layers and error back propagation algorithm with convex optimization structure trains these multiple feature layers. Testing with different flower images sets from multiple sources resulted in average classification accuracy of 92% for shape, color and texture supervised active contour segmented flower images.

     


  • Keywords


    Flower Image Segmentation; Pattern Classification; Multi-Dimensional Features; Artificial Neural Networks.

  • References


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Article ID: 10795
 
DOI: 10.14419/ijet.v7i1.1.10795




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