Improving local learning for object categorization by exploring the effects of ranking

Tien Lung Chang*, Tyng Luh Liu, Jen-Hui Chuang

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

Local learning for classification is useful in dealing with various vision problems. One key factor for such approaches to be effective is to find good neighbors for the learning procedure. In this work, we describe a novel method to rank neighbors by learning a local distance function, and meanwhile to derive the local distance function by focusing on the high-ranked neighbors. The two aspects of considerations can be elegantly coupled through a well-defined objective function, motivated by a supervised ranking method called P-Norm Push. While the local distance functions are learned independently, they can be reshaped altogether so that their values can be directly compared. We apply the proposed method to the Caltech-101 dataset, and demonstrate the use of proper neighbors can improve the performance of classification techniques based on nearest-neighbor selection.

原文English
主出版物標題26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
DOIs
出版狀態Published - 2008
事件26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States
持續時間: 23 6月 200828 6月 2008

出版系列

名字26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Conference

Conference26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
國家/地區United States
城市Anchorage, AK
期間23/06/0828/06/08

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