Large scale multi-class classification using latent classifiers

We study the problem of multi-class image classification with large number of classes, of which the one-vs-all based approach is prohibitive in practical applications. Recent state-of-the-art approaches rely on label tree to reduce classification complexity. However, building optimal tree structures and learning precise classifiers to optimize tree loss is challenging. In this paper, we introduce a novel approach using latent classifiers that can achieve comparable speed but better performance. The key idea is that instead of using C one-vs-all classifiers (C is the number of classes) to generate the score matrix for label prediction, a much smaller number of classifiers are used. These classifiers, called latent classifiers, are generated by analyzing the correlation among classes and removing redundancy. Experiments on several large datasets including ImageNet-1K, SUN-397, and Caltech-256 show the efficiency of our approach.