Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation
We present a method to learn probabilistic object models (POMs) with minimal supervision, which exploit different visual cues and perform tasks such as classification, segmentation, and recognition. We formulate this as a structure induction and learning task and our strategy is to learn and combine...
| Main Authors: | , , , |
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| 其他作者: | |
| 格式: | 文件 |
| 语言: | en_US |
| 出版: |
Institute of Electrical and Electronics Engineers
2010
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| 在线阅读: | http://hdl.handle.net/1721.1/52348 |