Skip to main content.
Advanced search >
<< Back to previous page Print

<< Friday, November 30, 2012 >>


Remind me

Tell a friend

Add to my Google calendar (bCal)

Download to my calendar

Bookmark and ShareShare


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

Electrical Engineering and Computer Sciences (EECS)


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.

Joint work with Tomasz Trzcinski, Vincent Lepetit and Pascal Fua.


510-643-2614