Low-Rank Online Metric Learning

Abstract

Image classification is a key problem in computer vision community. Most of the conventional visual recognition systems usually train an image classifier in an offline batch mode with all training data provided in advance. Unfortunately in many practical applications, usually only a small amount of training samples are available in the initialization stage and many more would come sequentially during the online process. Because the image data characteristics could dramatically change over time, it is important for the classifier to adapt to the new data incrementally. In this chapter, we present an online metric learning model to address the online image classification/scene recognition problem via adaptive similarity measurement. Given a number of labeled samples followed by a sequential input of unseen testing samples, the similarity metric is learned to maximize the margin of the distance among different classes of samples. By considering the low-rank constraint, our online metric learning model not only provides competitive performance compared with the state-of-the-art methods, but also guarantees to converge. A bi-linear graph is also applied to model the pair-wise similarity, and an unseen sample is labeled depending on the graph-based label propagation, while the model can also self-update using the new samples that are more confident labeled. With the ability of online learning, our methodology can well handle the large-scale streaming video data with the ability of incremental self-update. We also demonstrate that the low-rank property widely exists in natural data. In the experiments, we evaluate our model to online scene categorization and experiments on various benchmark datasets and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our algorithm.