@article{scholars2255,
           pages = {45--59},
         journal = {Neural Processing Letters},
            year = {2011},
           title = {Distributed multi-feature recognition scheme for greyscale images},
             doi = {10.1007/s11063-010-9163-8},
          number = {1},
          volume = {33},
            note = {cited By 3},
        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. {\^A}{\copyright} 2010 Springer Science+Business Media, LLC.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79751533644&doi=10.1007\%2fs11063-010-9163-8&partnerID=40&md5=6c316509f80c70729c573e8d9f1270d6},
        keywords = {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},
          author = {Muhamad Amin, A. H. and Khan, A. I.},
            issn = {13704621}
}