Abstract
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.
Highlights
With the popularization and development of Internet technology, image recognition technology began to be applied to all aspects of life [1,2,3]
Computational Intelligence and Neuroscience models avoid the artificial selection of important parts and artificial feature extraction and reduce the impact of artificial factors on the recognition effect, the design of these deep learning models is very dependent on the color and texture information of the picture, which is difficult to be directly used in the recognition of hand-painted sketches lacking color and texture information. erefore, a neural network DSCN (Depthwise Separable Convolutions Net) based on depth separable convolution is proposed for hand-drawn sketch recognition
Compared with the classical deep learning model Alex-Net, the recognition accuracy is improved by 5.6%. e results show that the introduction of stroke order in hand-drawn sketch can effectively improve its recognition accuracy
Summary
With the popularization and development of Internet technology, image recognition technology began to be applied to all aspects of life [1,2,3]. Computational Intelligence and Neuroscience models avoid the artificial selection of important parts and artificial feature extraction and reduce the impact of artificial factors on the recognition effect, the design of these deep learning models is very dependent on the color and texture information of the picture, which is difficult to be directly used in the recognition of hand-painted sketches lacking color and texture information. In order to solve the problems of lack of color and texture information, incomplete contour, large dependence on human experience, and unsatisfactory recognition effect in hand-painted sketch recognition, this paper studies and proposes a method for hand-painted sketch recognition based on DSCN network. E third section first introduces the network structure based on DSCN and gives the application process of hand-drawn sketch recognition based on DSCN model. In order to solve the problems of lack of color and texture information, incomplete contour, large dependence on human experience, and unsatisfactory recognition effect in hand-painted sketch recognition, this paper studies and proposes a method for hand-painted sketch recognition based on DSCN network. e first section briefly introduces the background and motivation of hand-drawn sketch recognition. e second section briefly introduces the status of hand-drawn sketch recognition, discusses the problems to be solved in the current hand-drawn sketch recognition algorithm, and summarizes the work and methods of this paper. e third section first introduces the network structure based on DSCN and gives the application process of hand-drawn sketch recognition based on DSCN model. e fourth section selects the dataset of training and testing and determines the evaluation index of model recognition effect. en, six groups of control experiments are designed based on DSCN structure. e fifth section briefly summarizes the main conclusions of this paper
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