Parameter Prediction for RIU-LBP Based on PSO-BP Algorithm

摘要：

Local Binary Pattern (LBP) is one of the most popular feature extraction algorithms in face recognition with good performance. However, setting proper parameters for this algorithm is still an open question in pattern recognition. In most previous research, this problem is solved with experienced comparison tests. However, such tests might be constrained by certain database and application and thus lack of generalization ability. In this paper, based on our previous research on factor analysis of the Rotation Invariant Uniform LBP (RIU-LBP) feature, we propose a parameter prediction and selection method based on the Particle Swarm Optimizer-Back Propagation neural network (PSO-BP) for setting the dominant factor, i.e. the blocking number for RIU-LBP feature. Experimental results show that the proposed prediction method could effectively save the computation time in parameter selection.

关键词： Local binary pattern BP neural network Particle Swarm Optimizer Parameter prediction

作者: Ying Tan Yuchun FANG Gong Cheng Wang Dai

作者单位: School of Computer Engineering and Science, Shanghai University Shanghai, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1345-1349

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）