Line Drawing Retrieval Combining Classification CNN and Shape Context

Traditional line drawing retrieval technique is usually inaccurate or inefficient due to its dependent on user-defined shape features. To address these issues, this paper presents a novel line drawing retrieval method combined with classification convolution neural network(CNN) and shape context. First, a convolution neural network with large volume kernel is introduced, and the network weights can be trained by the classification task of the line drawing image dataset. The convolution feature information of each line drawing is also extracted with the network structure. Then, according to the input simple line drawing that the user draws on the drawing board, the top 15 kinds of classification are obtained by the two-step classification using CNN, and the similarity of 15 classes is matched with the shape context from which the top 8 classes can be chosen. Finally, the convolution neural network is used to extract the feature information of the input line drawing and match with the top 8 classes using their similarity measure. Experimental results show that our method can efficiently and accurately retrieve the similar line drawings from the TU-Berlin sketch benchmark dataset under Caffe framework, which ensure that the retrieval results can be concentrated in the most similar image category and the same type has more choice to be selected.