A clustering approach to optimize online dictionary learning
Dictionary learning has emerged as a powerful tool for low level image processing tasks such as denoising and inpainting, as well as sparse coding and representation of images. While there has been extensive work on the development of online and offline dictionary learning algorithms to perform the aforementioned tasks, the problem of choosing an appropriate dictionary size is not as widely addressed. In this paper, we introduce a new scheme to reduce and optimize dictionary size in an online setting by synthesizing new atoms from multiple previous ones. We show that this method performs as well as existing offline and online dictionary learning algorithms in terms of representation accuracy while achieving significant speedup in dictionary reconstruction and image encoding times. Our method not only helps in choosing smaller and more representative dictionaries, but also enables learning of more incoherent ones.