Object Classification with Local Features
This paper presents a novel method for classify objects using local features in cluttered real-world scenes.After analyzing of several fashionable local features at present,we choose the suitable features to construct the visual vocabulary.These visual words are invariant to image scale and rotation,and are shown robust to addition of noise and changes in 3D viewpoint.We also describe two approaches to represent objects using these visual words.As baselines for comparison,some additional classification systems also have been implemented.The performance analysis on the obtained experimental results demonstrates that the proposed methods are effective and efficient.
object classification local features descriptor classifier clustering
Jian Cao Hongqian Chen Huijun Ma Yong Wang
College of Computer and Information Engineering Beijing Technology and Business University Beijing.1 Business School of Beijing Technology and Business University Beijing,100048,China
国际会议
太原
英文
553-556
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）