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Technical Program
Paper Detail
| Paper: | TA-L1.4 |
| Session: | Image Segmentation |
| Time: | Tuesday, October 10, 10:40 - 11:00 |
| Presentation: |
Lecture
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| Title: |
UNSUPERVISED IMAGE LAYOUT EXTRACTION |
| Authors: |
David Liu; Carnegie Mellon University | | | | | Datong Chen; Carnegie Mellon University | | | | | Tsuhan Chen; Carnegie Mellon University | | |
| Abstract: |
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, aspect-feature 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. |
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