Open AccessFlower image classification with basket of features and multi layered artificial neural networks
Author(s) -
Syed Inthiyaz,
B. T. P. Madhav,
Ch. Raghava Prasad
Publication year - 2017
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.1.10795
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , artificial neural network , feature (linguistics) , segmentation , backpropagation , image segmentation , feature vector , wavelet , active contour model , contextual image classification , computer vision , image (mathematics) , philosophy , linguistics
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.