Object recognition using a bio-inspired neuron model with bottom-up and top-down pathways |
| |
Authors: | Yuhua ZhengAuthor Vitae Yan MengAuthor Vitae |
| |
Affiliation: | a The Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA b The Department of Computing, University of Surrey, Guildford, Surrey, GU2 7XH, UK |
| |
Abstract: | 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. |
| |
Keywords: | Neural networks Bottom-up and top-down pathways Learning Object recognition Multi-scale histogram of oriented gradients |
本文献已被 ScienceDirect 等数据库收录! |
|