Hough Voting with Distinctive Mid-Level Parts for Object Detection

摘要：

　　This paper presents an efficient method for object detection in natural scenes.It is accomplished via generalized Hough transform of distinctive midlevel parts.These parts are more meaningful than low-level patches such as lines or corners and would be able to cover the key structures of object.We collect the initial sets of parts by clustering with k-means in WHO space and train LDA model for every cluster.The codebooks are generated by applying the trained detectors to discover parts in whole positive training images and storing their spatial distribution relative to object center.When detecting in a new image,the energy map is formed by the voting from every entry in codebook and is used to predict the location of object.Experiment result shows the effectiveness of the proposed scheme.

关键词： Object detection Hough Voting Mid-level Parts LDA

作者: Xiaoqin Kuang Nong Sang Feifei Chen Runmin Wang Changxin Gao

作者单位: Science and Technology on Multi-spectral Information Processing Laboratory,School of Automation,Huazhong University of Science and Technology,Wuhan,China,430074

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 305-313

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）