A Strategy of Car Detection via Sparse Dictionary

摘要：

In recent years there is a growing interest in the study of sparse representation for object detection. These approaches heavily depend on local salient image patches, thus weakening the global contribution to the object identification of other less informative signals.Our generic approach not only employs the informative representation by linear transform, but also keeps all the spatial dependence implied among the objects. As an example,car images can be represented using parts from a vocabulary, along with spatial relations observed among them.Our approach is conducted with the quantitative measurement in developing the car detector at every stage. The theory underneath the optimal solution is the maximal mutual information carried out by the system. Our goal is to keep the maximal mutual information transmitted from stage to stage so that only the least uncertainty about the class identification remains based on the observation of classifiers output.

关键词： Scaling factor Sparse representation Mutual information Object detection

作者: JIN Guo-Qing Dong Ying-Hui

作者单位: College of Electronic Engineering , Naval Univ. of Engineering,WuHan 430033,China

会议类型: 国际会议

会议名称: Third International Conference on Digital Image Processing(ICDIP 2011)(第三届数字图像处理国际会议)

会议地点: 成都

会议语种:英文

页码: 446-450

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）