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Abstract: This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well-known. This is addressed here by using a fully probabilistic Relevance Vector Machine (RVM) to generate observations with Gaussian... (Update)
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BibTeX entry: (Update)
@misc{ visual-sparse,
author = "For Efficient Visual",
title = "Sparse Bayesian Learning",
url = "citeseer.ist.psu.edu/759493.html" }
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