Semi-supervised Metric Learning for Image Classification

Abstract

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.