Large-scale Dictionary Learning For Local Coordinate Coding

Abstract

Local coordinate coding has recently been introduced to learning visual feature dictionary and achieved top level performance for object recognition. However, the computational complexity scales linearly with the number of samples, so it does not scale up well for large-scale databases. In this paper, we propose an online learning algorithm which, at every iteration round, only processes one or a mini-batch of random samples (e.g., two hundred samples). Our algorithm theoretically ensures the convergence to the expected objective at infinity. Experiments on object recognition demonstrate the advantage over the original local coordinate coding method in terms of efficiency with comparable performance.