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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Attribute Discovery via Predictable Discriminative Binary Codes
verfasst von : Mohammad Rastegari, Ali Farhadi, David Forsyth
Erschienen in: Computer Vision – ECCV 2012
Verlag: Springer Berlin Heidelberg
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We present images with binary codes in a way that balances discrimination and learnability of the codes. In our method, each image claims its own code in a way that maintains discrimination while being predictable from visual data. Category memberships are usually good proxies for visual similarity but should not be enforced as a hard constraint. Our method learns codes that maximize separability of categories unless there is strong visual evidence against it. Simple linear SVMs can achieve state-of-the-art results with our short codes. In fact, our method produces state-of-the-art results on Caltech256 with only 128-dimensional bit vectors and outperforms state of the art by using longer codes. We also evaluate our method on ImageNet and show that our method outperforms state-of-the-art binary code methods on this large scale dataset. Lastly, our codes can discover a discriminative set of attributes.