標題: Improving local learning for object categorization by exploring the effects of ranking
作者: Chang, Tien-Lung
Liu, Tyng-Luh
Chuang, Jen-Hui
資訊工程學系
Department of Computer Science
公開日期: 2008
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.
URI: http://hdl.handle.net/11536/135042
ISBN: 978-1-4244-2242-5
ISSN: 1063-6919
期刊: 2008 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-12
起始頁: 2190
結束頁: +
顯示於類別:會議論文