Unsupervised Image Layout Extraction

15 years 1 months ago

Download www.andrew.cmu.edu

We propose a novel unsupervised learning algorithm to extract the layout of an image by learning latent object-related aspects. Unlike traditional image segmentation algorithms that segment an image using feature similarity, our method is able to learn high-level object characteristics (aspects) from a large number of unlabelled images containing similar objects to facilitate image segmentation. Our method does not require human to annotate the training set and works without supervision. We use a graphical model to address the learning of aspects and layout extraction together. In particular, aspectfeature dependency from multiple images is learned via the Expectation-Maximization algorithm. We demonstrate that, by associating latent aspects to spatial structure, the proposed method achieves much better layout extraction results than using Probabilistic Latent Semantic Analysis.

David Liu, Datong Chen, Tsuhan Chen

Real-time Traffic