Title :
Texture classification using a two-stage neural network approach
Author :
Raghu, P.P. ; Poongodi, R. ; Yegnanarayana, B.
Author_Institution :
Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Madras, India
Abstract :
In this article, we present a two stage neural network structure which combines the self-organizing map (SOM) and the multilayer perceptron (MLP) for the problem of texture classification. The texture features are extracted using a multichannel approach. These channels comprise of a set of Gabor filters having different sizes, orientations and frequencies to constitute N-dimensional feature vectors. The SOM acts as a clustering mechanism to map these N-dimensional feature vectors onto a 2D space. This in turn forms the feature space to feed into MLP for training and subsequent classification. It is shown that this mechanism increases the inter-class separation and decreases the intra-class distance in the feature space, hence reduces the classification complexity. Also, the reduction in the dimensionality of the feature space results in reduction of the learning time of the MLP.
Keywords :
feature extraction; image classification; image texture; learning (artificial intelligence); multilayer perceptrons; self-organising feature maps; Gabor filters; clustering; feature extraction; feature space; feature vectors; inter-class separat; intra-class distance; learning time; multilayer perceptron; self-organizing map; texture classification; two-stage neural network; Artificial neural networks; Computer science; Feature extraction; Feeds; Frequency; Gabor filters; Image texture analysis; Multi-layer neural network; Multilayer perceptrons; Neural networks;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.714161