Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16219
Title: Genetic evolution processing of data structures for image classification
Authors: Cho, SY
Chi, Z 
Keywords: Adaptive processing of data structures
Genetic algorithm
Image classification
Neural networks
Issue Date: 2005
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on knowledge and data engineering, 2005, v. 17, no. 2, p. 216-231 How to cite?
Journal: IEEE transactions on knowledge and data engineering 
Abstract: This paper describes a method of structural pattern recognition based on a genetic evolution processing of data structures with neural networks representation. Conventionally, one of the most popular learning formulations of data structure processing is Backpropagation Through Structures (BPTS) [7]. The BPTS algorithm has been successfully applied to a number of learning tasks that involved structural patterns such as image, shape, and texture classifications. However, this BPTS typed algorithm suffers from the long-term dependency problem in learning very deep tree structures. In this paper, we propose the genetic evolution for this data structures processing. The idea of this algorithm is to tune the learning parameters by the genetic evolution with specified chromosome structures. Also, the fitness evaluation as well as the adaptive crossover and mutation for this structural genetic processing are investigated in this paper. An application to flowers image classification by a structural representation is provided for the validation of our method. The obtained results significantly support the capabilities of our proposed approach to classify and recognize flowers in terms of generalization and noise robustness.
URI: http://hdl.handle.net/10397/16219
ISSN: 1041-4347 (print)
1558-2191 (online)
DOI: 10.1109/TKDE.2005.28
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