Tomasz Piotr Trzcinski

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Binary descriptors allow faster similarity computation than real-valued ones while requiring much less storage. As a result, many algorithms have recently been proposed to binarize ﬂoating-point descriptors so that they can be searched for quickly. Unfortu ...

We propose a novel and general framework to learn compact but highly discriminative floating-point and binary local feature descriptors. By leveraging the boosting-trick we first show how to efficiently train a compact floating-point descriptor that is ver ...

Binary keypoint descriptors provide an efficient alternative to their floating-point competitors as they enable faster processing while requiring less memory. In this paper, we propose a novel framework to learn an extremely compact binary descriptor we ca ...

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively repre- sent a s ...

Tomasz Piotr Trzcinski

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Thick Boundaries in Binary Space and Their Influence on Nearest-Neighbor Search

Learning Image Descriptors with Boosting

Boosting Binary Keypoint Descriptors

Learning and Matching Binary Local Feature Descriptors

Learning Image Descriptors with the Boosting-Trick