Sketch classification with deep learning models

Abstract

Sketch classification problem is challenging due to several reasons, such as absence of color and texture information, lack of detailed information of objects, and the quality, which depends on drawing ability of the person. In this study, sketch classification problem is addressed by using deep convolutional neural network models. Specifically, the effect of domain adaptation is examined, when fine-tuning the convolutional neural networks for sketch classification. By employing domain adaptation, the classification accuracy is increased by around 3%. The proposed system, which utilizes VGG-16 network model and performs two-stage fine-tuning, outperforms the previous state-of-the-art approaches on the TU Berlin sketch dataset by reaching 79,72% accuracy.