一种基于多特征的双阶段草图分类方法

Two-stages Sketch Classification Method Based on Multi-features

针对普通的卷积神经网络不能充分利用草图的细粒度特征以及轮廓特征,分类效果不理想的问题,提出了一种基于多特征的双阶段草图分类方法,将草图粗粒度特征、细粒度特征与轮廓特征的分类结果相融合.该方法分两个阶段进行训练,对特征的提取更加充分.在初训练阶段,将草图图像通过卷积神经网络获得草图的粗粒度特征分类结果,引入双线性池化以获得草图的细粒度特征分类结果,提取草图的轮廓图像以获得草图的轮廓特征分类结果;在再训练阶段,提出了一种可训练的分类结果融合模块,将各特征分类结果进行动态地融合,并提出了一个正则化项以减缓该融合模块的过拟合.将该方法与TUBerlin数据集上的几种最新方法进行了比较,实验结果证明了所提出方法的有效性.

Aiming at the problem that the ordinary convolutional neural network cannot make full use of the fine-grained feature and contour feature of sketch,and the classification effect is not ideal,a two-stages sketch classification method based on multi features is proposed in this paper.This method combines the classification results of the coarse-grained feature,fine-grained feature and contour feature,and is trained in two stages to extract more sufficient features.In the first training stage,the classification results of sketch coarse-grained feature are obtained directly by convolution neural network;the classification results of sketch fine-grained feature are obtained by adding bilinear pooling;the classification results of sketch contour feature are obtained by extracting the contour of the sketch.In the second training stage,a trainable classification result fusion module is proposed to fuse the classification results from the first training stage,and a regularization term is introduced to prevent the overfitting of it.The proposed method is compared with several latest methods on tuberlin dataset,and the experimental results show the effectiveness of the proposed method.