Feature Extraction and Image Recognition with Convolutional Neural Networks

The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1087; no. 6; pp. 62032 - 62038
Main Author Liu, Yu Han
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.09.2018
Subjects
Algorithms
Artificial neural networks
Feature extraction
Feature recognition
Neural networks
Object recognition
Physics
Structural hierarchy
Taste
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Summary:The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by animal visual system. Convolution serves as a perfect realization of an optic nerve cell which merely responds to its receptive field and it performs well in image feature extraction. Being highly-hierarchical networks, CNN is structured with a series of different functional layers. The function blocks are separated and described clearly by each layer in this paper. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1087/6/062032