中文摘要：

以素描教学过程中的临摹作品作为研究对象,将深度卷积特征应用于素描作品的分类与评价中.首先测试深度卷积特征在素描作品分类中的效果,同时将素描作品评价问题转换为基于作品的构图、形准、质感、画面整体黑白灰等图像高阶语义特征的细分类问题(优、良、中、差);然后提出双线性卷积模型,以较好地解决图像细分类问题;最后使用TensorSketch投影算法将双线性深度卷积特征进行压缩,并采用端到端的训练进行模型微调.实验结果表明,在素描作品分类任务中,深度卷积特征明显优于传统手工特征(如直方图特征、纹理特征和SIFT特征);在素描作品评价中,压缩的双线性深度卷积特征能在较低维度上达到相似的评价效果.

英文摘要：

We apply deep convolutional features in the classification and evaluation of sketch works which are collected from the sketch teaching scenario.Firstly,we test the classification performance of sketch works using deep convolutional features,and convert the evaluation task of sketch works to the fine-grained classification task(four classes:best,good,moderate and worst)based on images'high-level semantic features(e.g.shape,composition,texture,proportion of black,white or gray and so on);secondly,we propose the bilinear convolutional neural network(CNN)model which is particularly useful for fine-grained categorization classification;finally,we use Tensor Sketch Projection to compact the bilinear CNN features and apply end-to-end training to fine-tune the model.The results demonstrate that:CNN features significantly outperform the traditional features(e.g.Histogram,LBP and SIFT)in sketch works classification task;Compact bilinear CNN features can achieve the similar results under lower dimension in sketch works evaluation task.