Unsupervised texture classification using vector quantization and deterministic relaxation neural network

Raghu, P. P. ; Poongodi, R. ; Yegnanarayana, B. (1997) Unsupervised texture classification using vector quantization and deterministic relaxation neural network IEEE Transactions on Image Processing, 6 (10). pp. 1376-1387. ISSN 1057-7149

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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

Related URL: http://dx.doi.org/10.1109/83.624953

Abstract

This paper describes the use of a neural network architecture for classifying textured images in an unsupervised manner using image-specific constraints. The texture features are extracted by using two-dimensional (2-D) Gabor filters arranged as a set of wavelet bases. The classification model comprises feature quantization, partition, and competition processes. The feature quantization process uses a vector quantizer to quantize the features into codevectors, where the probability of grouping the vectors is modeled as Gibbs distribution. A set of label constraints for each pixel in the image are provided by the partition and competition processes. An energy function corresponding to the a posteriori probability is derived from these processes, and a neural network is used to represent this energy function. The state of the network and the codevectors of the vector quantizer are iteratively adjusted using a deterministic relaxation procedure until a stable state is reached. The final equilibrium state of the vector quantizer gives a classification of the textured image. A cluster validity measure based on modified Hubert index is used to determine the optimal number of texture classes in the image.

Item Type:Article
Source:Copyright of this article belongs to IEEE.
ID Code:57770
Deposited On:29 Aug 2011 11:51
Last Modified:29 Aug 2011 11:51

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