Abstract
We consider the problem of detecting a large number of different classes of objects in cluttered scenes. We present a learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). Shared features, emerge in a model of object recognition trained to detect many object classes efficiently and robustly, and are preferred over class-specific features. Although that class-specific features achieve a more compact representation for a single category, the whole set of shared features is able to provide more efficient and robust representations when the system is trained to detect many object classes than the set of class-specific features. Classifiers based on shared features need less training data, since many classes share similar features (e.g., computer screens and posters can both be distinguished from the background by looking for the feature “edges in a rectangular arrangement”).
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Torralba, A., Murphy, K.P., Freeman, W.T. (2006). Shared Features for Multiclass Object Detection. 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_18
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DOI: https://doi.org/10.1007/11957959_18
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