Joint group kernel sparse coding based multimodal material perception and identification method
HE Kongfei, XIONG Pengwen, TONG Xiaobao
School of Information Engineering, Nanchang University, Nanchang 330031, China
Abstract: Machine vision-based methods are often difficult to distinguish materials with highly similar appearance. Therefore, it is necessary to overcome the defects of visual methods by integrating other modal information. In order to solve this problem, firstly, a series of appropriate similarity evaluation methods are introduced according to the properties of each modality. Secondly, the joint group kernel sparse coding method is used to fuse the inseparability multimodal data, and the solution method of this model is introduced in detail. Finally, a comparison experiment was conducted on the public data set containing 184 materials. The experimental results showed that the accuracy of coarse, medium and fine classification of the used fusion framework is 90.8%, 76.6% and 73.4%, respectively, which could significantly improve the recognition effect compared with the visual method.
Keywords: pattern recognition and intelligent system;sparse joint group lasso;multi-modality fusion;material classification
2020, 46(12):129-134  收稿日期: 2020-11-17;收到修改稿日期: 2020-12-02
基金项目: 国家自然科学基金资助项目（61903175，61663027）；江西省主要学科学术和技术带头人项目（20204BCJ23006）；江西省研究生创新专项资金资助项目（YC2019-S011，YC2020-S101）
作者简介: 何孔飞(1994-),男,安徽铜陵市人,硕士研究生,专业方向为智能机器人
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