Novel Spatial Pyramid Matching for Scene and Object Classification

摘要：

It is difficult to classify object or scene images with high accuracy when the dataset is relatively large. Spatial Pyramid Matching (SPM) was proposed to deal with this problem, but there are some shortages. As an improvement for SPM, we proposed three pieces of meliorations: first, use approximate nearest neighbor method instead of k-means for clustering; second, regulate the size of codebook referring to quantity and pixels of the images, by calculating sub-codebook for every category and eliminating the codes which are nearer to the registered ones than the threshold; third, rescale the histogram features, and classify the scene with hierarchical strategy. Experiments prove that our approach make better performance than other state-of-the-art classification methods using just one matching kernel.

关键词： Object Classification Spatial Pyramid Matching ANN clustering

作者: Kai Ding Weihai Chen Xingming Wu Zhong Liu

作者单位: School of Automation Science and Electrical Engineering,Beijing University of Aeronautics and Astron School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astro

会议类型: 国际会议

会议名称: IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)

会议地点: 北京

会议语种:英文

页码: 172-177

在线出版日期: 2012-07-25（万方平台首次上网日期，不代表论文的发表时间）