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Learning Image Descriptors with the Boosting-Trick: VCL Lunch Talk
Seminar | November 30 | 12-1 p.m. | Soda Hall, Visual Computing Lab - 510 Soda
Mario Christoudias, EECS/UC Berkeley
Representing salient image regions in a way that is invariant to unwanted image transformations is a crucial computer vision task. In this talk I will present work on learning an invariant image description jointly optimized over both the descriptor weighting and pooling configuration with boosting. Similar to the kernel-trick, the resulting boosting-trick results in a non-linear transformation of the image patch, however, as I will show can be used to define similarity functions better suited for local patch description. We employ gradient-based weak learners that yields a learned descriptor that closely resembles the well-known SIFT. The resulting descriptor can be learned directly from raw intensity patches achieving state-of-the-art performance. I will present results on a benchmark local patch dataset that exhibits large variations in lighting and viewpoint demonstrating the effectiveness of the proposed approach.
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