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Consistent Image Analogies using Semi-supervised Learning
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2008 |
| Abstract | In this paper we study the following problem: given two source images A and A′, and a target image B, can we learn to synthesize a new image B′ which relates to B in the same way that A′ relates to A? We propose an algorithm which a) uses a semi-supervised component to exploit the fact that the target image B is available apriori, b) uses inference on a Markov Random Field (MRF) to ensure global consistency, and c) uses image quilting to ensure local consistency. Our algorithm can also deal with the case when A is only partially labeled, that is, only small parts of A′ are available for training. Empirical evaluation shows that our algorithm consistently produces visually pleasing results, outperforming the state of the art. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://users.soe.ucsc.edu/~vishy/pubs/CheVisZha08.pdf |
| Alternate Webpage(s) | http://www.stat.purdue.edu/~vishy/papers/CheVisZha08.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Apriori algorithm Emoticon Image analogy Inference Local consistency Markov chain Markov random field Semi-supervised learning Semiconductor industry Supervised learning Tracer |
| Content Type | Text |
| Resource Type | Article |
