TY - GEN
T1 - Semi-supervised metric learning for image classification
AU - Hu, Jiwei
AU - Sun, Chen Sheng
AU - Lam, Kin Man
PY - 2010/11/9
Y1 - 2010/11/9
N2 - The k-nearest neighbor (KNN) classifier is a simple and effective method for image classification. However, its performance significantly depends on how the distance between samples is calculated. Therefore, learning an appropriate distance metric is the most important issue for the KNN-based classifiers. The distance metric can be learned from either labeled or unlabeled data. Labeled images are expensive to generate, while unlabeled images are abundant, and the label information is crucial for the performance of the learned metric. In this work, we present a semi-supervised method for learning the distance metric. We propose a semi-supervised extension to the Neighborhood Component Analysis (NCA) method, which is a supervised method especially tailored for KNN classifiers. Then, we use the learned distance metric to classify images using the KNN method. Experiment shows that our proposed method outperforms both the traditional supervised and unsupervised methods.
AB - The k-nearest neighbor (KNN) classifier is a simple and effective method for image classification. However, its performance significantly depends on how the distance between samples is calculated. Therefore, learning an appropriate distance metric is the most important issue for the KNN-based classifiers. The distance metric can be learned from either labeled or unlabeled data. Labeled images are expensive to generate, while unlabeled images are abundant, and the label information is crucial for the performance of the learned metric. In this work, we present a semi-supervised method for learning the distance metric. We propose a semi-supervised extension to the Neighborhood Component Analysis (NCA) method, which is a supervised method especially tailored for KNN classifiers. Then, we use the learned distance metric to classify images using the KNN method. Experiment shows that our proposed method outperforms both the traditional supervised and unsupervised methods.
KW - distance metric
KW - K-nearest neighbor
KW - neighborhood component analysis
KW - semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=78049450022&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15696-0_67
DO - 10.1007/978-3-642-15696-0_67
M3 - Conference article published in proceeding or book
SN - 3642156959
SN - 9783642156953
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 728
EP - 735
BT - Advances in Multimedia Information Processing, PCM 2010 - 11th Pacific Rim Conference on Multimedia, Proceedings
T2 - 11th Pacific Rim Conference on Multimedia, PCM 2010
Y2 - 21 September 2010 through 24 September 2010
ER -