%0 Journal Article
%@ 13704621
%A Muhamad Amin, A.H.
%A Khan, A.I.
%D 2011
%F scholars:2255
%J Neural Processing Letters
%K Back propagation neural networks; Classification accuracy; Coarse-grained; Collaborative approach; Computational approach; Computational networks; Distributed process; Facial images; Feature recognition; Grey scale images; Greyscale; Hierarchical graphs; Learning patterns; Multi-class; Parallel and distributed processing; Recognition accuracy; Recognition process; Single cycle; Stored pattern; Sub-network; Tightly-coupled, Distributed parameter networks; Image recognition; Neural networks, Feature extraction
%N 1
%P 45-59
%R 10.1007/s11063-010-9163-8
%T Distributed multi-feature recognition scheme for greyscale images
%U https://khub.utp.edu.my/scholars/2255/
%V 33
%X 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. © 2010 Springer Science+Business Media, LLC.
%Z cited By 3