Distributed Multi-Feature Recognition Scheme for Greyscale Images

Muhamad Amin , Anang Hudaya and Khan, Asad I. (2011) Distributed Multi-Feature Recognition Scheme for Greyscale Images. Neural Processing Letters, 33 (1). pp. 45-59.

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Contemporary image recognition schemes either rely on single-feature recognition or focus on solving multi-feature recognition using complex computational approaches. Furthermore these approaches tend to be of tightly-coupled nature, thus not readily deployable within computational networks. Distributed Hierarchical Graph Neuron (DHGN) is a distributed single-cycle learning pattern recognition algorithm that can scale from coarse-grained to fine-grained networks and it has comparable accuracy to contemporary image recognition schemes. In this paper, we present an implementation of DHGN that works for multi-feature recognition of images. Our scheme is able to disseminate recognition of each feature within an image to a separate computational subnetwork. Thereby allowing a number of features being analysed simultaneously using a uniform recognition process. We have conducted tests on a collection of greyscale facial images. The results show that our approach produces high recognition accuracy through a simple distributed process. Furthermore, our approach implements single-cycle learning known as collaborative-comparison learning where new patterns are continuously stored using collaborative approach without affecting previously stored patterns. Our proposed scheme demonstrates higher classification accuracy in comparison with Back-Propagation Neural Network for multi-class images.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Departments / MOR / COE: Departments > Computer Information Sciences
Depositing User: Dr. Anang Hudaya Muhamad Amin
Date Deposited: 30 May 2011 13:10
Last Modified: 19 Jan 2017 08:23
URI: http://scholars.utp.edu.my/id/eprint/5558

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