Abstract
This chapter presents a principled way of formulating models for automatic local feature selection in object class recognition when there is little supervised data. Moreover, it discusses how one could formulate sensible spatial image context models using a conditional random field for integrating local features and segmentation cues (superpixels). By adopting sparse kernel methods and Bayesian model selection and data association, the proposed model identifies the most relevant sets of local features for recognizing object classes, achieves performance comparable to the fully supervised setting, and consistently outperforms existing methods for image classification.
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Carbonetto, P., Dorkó, G., Schmid, C., Kück, H., de Freitas, N. (2006). A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11957959_15
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DOI: https://doi.org/10.1007/11957959_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68794-8
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