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• W3008842153 abstract "Numerous methods have been proposed to solve the problem of visual image classification in the past few years, but few of them are associated with human brain memory mechanism. In this paper, we study the mechanism of human brain auto-associative memory and apply it to the storage, retrieval, and classification of visual images. The learning process of the memory model is based on Hebb-like associative local learning rule and the recall rests on a normalized dot product operation. The proposed model is capable of storing and retrieving visual images as human brain, and it can achieve distributed storage and incremental learning. The experimental results on some benchmark image databases indicate that the proposed model outperforms previously proposed Pattern Association Memory Model (PAMM) and Self-Organizing Incremental Neural Network (SOINN) on recognition accuracy." @default.
• W3008842153 created "2020-03-06" @default.
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• W3008842153 date "2019-12-01" @default.
• W3008842153 modified "2023-09-27" @default.
• W3008842153 title "Visual Image Classification Based on Auto-Associative Memory" @default.
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• W3008842153 doi "https://doi.org/10.1109/ssci44817.2019.9002999" @default.
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