融合深度学习和语义树的草图识别方法

Sketch Recognition Combining Deep Learning and Semantic Tree

现有的草图识别框架利用整幅图像作为网络输入,草图识别过程可解释性较差。文中融合深度学习和语义树,提出草图语义网(Sketch-Semantic Net)。首先对草图进行部件分割,将单幅完整的草图分割为多个具有语义概念的部件图。然后利用深度迁移学习识别草图部件。最后通过语义树的语义概念关联部件同部件所属草图对象类别,较好地弥补sketch 图像从底层语义到高层语义之间的语义鸿沟。在广泛应用的草图分割数据集上的实验验证文中方法的有效性。

In the existing sketch recognition based on deep learning,a whole sketch is employed as an input of network,and therefore the recognition process is uninterpretable.The semantic tree is introduced into sketch recognition based on deep learning,and a sketch recognition method,sketch- semantic net,is proposed in this paper.Firstly,data-driven segmentation method is utilized to divide a whole sketch into component sketches with the semantic information.Secondly,the component sketches are recognized by transfer deep learning.Finally,the component sketches are associated with the sketch categories according to the semantic concepts of the semantic tree,and thus the gap between low level semantics and high level semantics is reduced.The experimental results on the popular Sketch_ dataset demonstrate the effectiveness of the proposed method.