Enhanced deep feature representation for patent image classification

Gege Song; Xianglin Huang; Gang Cao; Wei Liu; Jianglong Zhang; Lifang Yang

doi:10.1117/12.2524360

6 May 2019 Enhanced deep feature representation for patent image classification

Gege Song, Xianglin Huang, Gang Cao, Wei Liu, Jianglong Zhang, Lifang Yang

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110690P (2019) https://doi.org/10.1117/12.2524360
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Automatic classification of digital patent images is significant for improving the efficiency of patent examination and management. In this paper, we propose a new patent image classification method based on an enhanced deep feature representation. Convolutional neural networks (CNN) is novelly applied to the patent image classification. The synergy between deep learning and traditional handcraft feature is explored. Specifically, the deep feature is first learned from massive patent image samples by AlexNet. Then such deep learning feature is further enhanced by fusing with two kinds of typical handcraft features including local binary pattern (LBP) and adaptive hierarchical density histogram (AHDH). In order to obtain a more compact feature representation, dimension of the fused feature is subsequently reduced by PCA. Finally, the patent image classification is conducted by a series of SVM classifier. Statistical test results on a large-scale image set show that the state-of-the-art performance is achieved by our proposed patent image classification method.

Citation Download Citation

Gege Song, Xianglin Huang, Gang Cao, Wei Liu, Jianglong Zhang, and Lifang Yang "Enhanced deep feature representation for patent image classification", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690P (6 May 2019); https://doi.org/10.1117/12.2524360

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