基于时序特征的草图识别方法

草图识别是一项很具有挑战性的工作。目前,大部分草图识别的工作都将草图当作普通的纹理图像,忽视了草图的时序性。因此,文中通过挖掘草图的时序性,将草图笔画按照时间分组。为进一步利用时序特征在草图识别过程中的作用,使用了循环神经网络将笔画分组按照时间序列作为输入,最后使用联合贝叶斯将各个时序下获得的草图特征进行整合,完成草图的识别工作。在公开标准数据集上对所提算法进行了测试,实验结果显示该算法的识别准确率明显高于其他算法。

Sequential Feature Based Sketch Recognition

Recognizing freehand sketches is a greatly challenging work.Most existing methods treat sketches as traditional texture images with fixed structural ordering and ignore the temporality of sketch.In this paper,a novel sketch recognition method was proposed based on the sequence of sketch.Strokes are divided into groups and their features are fed into recurrent neural network to make use of the temporality.The features from each temporality are combined to produce the final classification results.The proposed algorithm was tested on a benchmark,and the recognition rate is far above other methods.