Modeling Image Patches with a Generic Dictionary of Mini-Epitomes.

ABSTRACT: The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting. Key ingredient of the proposed model is a compact dictionary of mini-epitomes, learned in an unsupervised fashion on a large collection of images. The use of epitomes allows us to explicitly account for photometric and position variability in image appearance. We show that this flexibility considerably increases the capacity of the dictionary to accurately approximate the appearance of image patches and support recognition tasks. For image classificatio

SUBMITTER: Papandreou G 

PROVIDER: S-EPMC4550088 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature