An Improved Method for Visual Word Generation Based on Kernel Function

摘要：

This paper studies a novel visual word generation method in the Bag-of-words model for object categorization. The conventional Bag-of-words algorithm represents the cluster centers as visual words, which led to the incomplete expressions of image semantic information, so an improved method for visual word generation using the soft-decision based on kernel function is proposed. First, SIFT keypoints of images are extracted. Then, after clustering SIFT keypoints, some typical SIFT keypoints are selected from a cluster by kernel density estimation using a kernel function. Finally, these selected keypoints are trained employing SVM to generate a visual word of this cluster. Experimental results show that the proposed visual word generation method enhances the expressions of image semantic information, increases the recall ratio effectively, and improves significantly the effect of object categorization.

关键词： visual word clustering kernel function object categorization

作者: Hongxia wang Kejian Yang Feng Gao

作者单位: School of Computer Science & Technology, Wuhan University of Technology, Wuhan, Hubei, China School of Computer, Wuhan University, Wuhan, Hubei, China

会议类型: 国际会议

会议名称: 2011 3nd International Conference on Mechanical and Electronics Engineering(2011年第三届机械与电子工程国际会议 ICMEE2011)

会议地点: 合肥

会议语种:英文

页码: 166-169

在线出版日期: 2011-09-23（万方平台首次上网日期，不代表论文的发表时间）