relation: https://khub.utp.edu.my/scholars/2255/
title: Distributed multi-feature recognition scheme for greyscale images
creator: Muhamad Amin, A.H.
creator: Khan, A.I.
description: 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.
date: 2011
type: Article
type: PeerReviewed
identifier:   Muhamad Amin, A.H. and Khan, A.I.  (2011) Distributed multi-feature recognition scheme for greyscale images.  Neural Processing Letters, 33 (1).  pp. 45-59.  ISSN 13704621     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79751533644&doi=10.1007%2fs11063-010-9163-8&partnerID=40&md5=6c316509f80c70729c573e8d9f1270d6
relation: 10.1007/s11063-010-9163-8
identifier: 10.1007/s11063-010-9163-8