Efficient image classification using neural networks and multiresolution analysis

Authors:
Andreas Tirakis;Stefanos Kollias
Affiliations:
Electrical Engineering Department, National Technical University of Athens, Athens, Greece;Electrical Engineering Department, National Technical University of Athens, Athens, Greece
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

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Abstract

In this paper, we investigate a new efficient image classification strategy. We propose a multiresolution analysis of the images to be classified and use of feedforward neural networks to classify the images at various lower resolutions. This approach results in a major reduction of the networks' interconnection weights as well as the required learning times. The proposed approach is applied first to the images of the lowest resolution; if the classification results are not acceptable, it is successively repeated to the next images of higher resolution. A neural network architecture which incorporates most of the interconnection weights already computed at the lower level (i.e. the knowledge already aquired by the network of the previous resolution level) is proposed for this purpose. Experimental results illustrate the efficiency of the proposed multiresolution classification procedure in a real life application.