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Distributed Multi-Feature Recognition Scheme for Greyscale Images.
- Source :
- Neural Processing Letters; Feb2011, Vol. 33 Issue 1, p45-59, 15p, 3 Charts, 12 Graphs
- Publication Year :
- 2011
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13704621
- Volume :
- 33
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Neural Processing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 57318480
- Full Text :
- https://doi.org/10.1007/s11063-010-9163-8