Graduate School, Dankook University, Yongin-si 16890, Gyeonggi-do, Republic of Korea.
Comput Intell Neurosci. 2021 Nov 18;2021:4056454. doi: 10.1155/2021/4056454. eCollection 2021.
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.
随着图像识别技术的飞速发展，自由手绘识别越来越受到关注。如何在没有颜色和纹理信息的情况下实现良好的识别效果，是自由手绘识别发展的关键。传统的非学习经典模型高度依赖于手动选择特征。为了解决这个问题，本文提出了一种基于 DSCN 结构的神经网络手绘识别方法。首先，绘制草图的笔画序列；然后，根据笔画序列结合神经网络提取特征，并将提取的图像特征作为模型的输入，构建不同图像特征之间的时间关系。通过在 TU-Berlin 数据集上的控制实验，结果表明，与传统的非学习方法 HOG-SVM、SIFT-Fisher Vector、MKL-SVM 和 FV-SP 相比，DSCN 网络的识别精度分别提高了 15.8%、10.3%、6.0%和 2.9%。与经典深度学习模型 Alex-Net 相比，识别精度提高了 5.6%。上述结果表明，本文提出的 DSCN 网络具有较强的特征提取和非线性表达能力，在引入笔画顺序后，能够有效提高手绘草图的识别精度。
