Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch

With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning...

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Autor principal:	Qunjing Ji
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/85b8ac7eb6c849429693b0b0f302e18c
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spelling	oai:doaj.org-article:85b8ac7eb6c849429693b0b0f302e18c2021-11-29T00:56:09ZResearch on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch1687-527310.1155/2021/4056454https://doaj.org/article/85b8ac7eb6c849429693b0b0f302e18c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4056454https://doaj.org/toc/1687-5273With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual selection features. To solve this problem, a neural network sketch recognition method based on DSCN structure is proposed in this paper. Firstly, the stroke sequence of the sketch is drawn; then, the feature is extracted according to the stroke sequence combined with neural network, and the extracted image features are used as the input of the model to construct the time relationship between different image features. Through the control experiment on TU-Berlin dataset, the results show that, compared with the traditional nonlearning methods, HOG-SVM, SIFT-Fisher Vector, MKL-SVM, and FV-SP, the recognition accuracy of DSCN network is improved by 15.8%, 10.3%, 6.0%, and 2.9%, respectively. Compared with the classical deep learning model, Alex-Net, the recognition accuracy is improved by 5.6%. The above results show that the DSCN network proposed in this paper has strong ability of feature extraction and nonlinear expression and can effectively improve the recognition accuracy of hand-painted sketches after introducing the stroke order.Qunjing JiHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Qunjing Ji Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch
description	With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual selection features. To solve this problem, a neural network sketch recognition method based on DSCN structure is proposed in this paper. Firstly, the stroke sequence of the sketch is drawn; then, the feature is extracted according to the stroke sequence combined with neural network, and the extracted image features are used as the input of the model to construct the time relationship between different image features. Through the control experiment on TU-Berlin dataset, the results show that, compared with the traditional nonlearning methods, HOG-SVM, SIFT-Fisher Vector, MKL-SVM, and FV-SP, the recognition accuracy of DSCN network is improved by 15.8%, 10.3%, 6.0%, and 2.9%, respectively. Compared with the classical deep learning model, Alex-Net, the recognition accuracy is improved by 5.6%. The above results show that the DSCN network proposed in this paper has strong ability of feature extraction and nonlinear expression and can effectively improve the recognition accuracy of hand-painted sketches after introducing the stroke order.
format	article
author	Qunjing Ji
author_facet	Qunjing Ji
author_sort	Qunjing Ji
title	Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch
title_short	Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch
title_full	Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch
title_fullStr	Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch
title_full_unstemmed	Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch
title_sort	research on recognition effect of dscn network structure in hand-drawn sketch
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/85b8ac7eb6c849429693b0b0f302e18c
work_keys_str_mv	AT qunjingji researchonrecognitioneffectofdscnnetworkstructureinhanddrawnsketch
_version_	1718407718382862336

Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch

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