Object recognition using a bio-inspired neuron model with bottom-up and top-down pathways
Zheng, Y, Meng, Y and Jin, Y (2011) Object recognition using a bio-inspired neuron model with bottom-up and top-down pathways Neurocomputing, 74 (17). pp. 3158-3169.
Neurocomputing_submit.pdf - Accepted version Manuscript
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In this paper, a new artificial neural network model is proposed for visual object recognition, in which the bottom-up, sensory-driven pathway and top-down, expectation-driven pathway are fused in information processing and their corresponding weights are learned based on the fused neuron activities. During the supervised learning process, the target labels are applied to update the bottom-up synaptic weights of the neural network. Meanwhile, the hypotheses generated by the bottom-up pathway produce expectations on sensory inputs through the top-down pathway. The expectations are constrained by the real data from the sensory inputs, which can be used to update the top-down synaptic weights accordingly. To further improve the visual object recognition performance, the multi-scale histograms of oriented gradients (MS-HOG) method is proposed to extract local features of visual objects from images. Extensive experiments on different image datasets demonstrate the efficiency and robustness of the proposed neural network model with features extracted using the MS-HOG method on visual object recognition compared with other state-of-the-art methods.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Date :||October 2011|
|Identification Number :||https://doi.org/10.1016/j.neucom.2011.04.020|
|Additional Information :||NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 74(17), October 2011, DOI 10.1016/j.neucom.2011.04.020.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||14 Dec 2011 13:48|
|Last Modified :||23 Sep 2013 18:49|
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