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Learning to recognize objects with little supervision
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DOI：10.1007/s11263-007-0067-7.png)
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Abstract
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This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object class recognition when there is little supervised data, and (iii) how to formulate sensible spatial image context models using a conditional random field for integrating local features and segmentation cues (superpixels). By adopting sparse kernel methods, Bayesian learning techniques and data association with constraints, the proposed model identifies the most relevant sets of local features for recognizing object classes, achieves performance comparable to the fully supervised setting, and obtains excellent results for image classification.
Keywords:
object recognition
scale-invariant keypoints
weakly supervised learning
data association
Bayesian analysis
Markov Chain Monte Carlo
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