TY  - CONF
AU  - Youssef El Rhabi
AU  - Simon Loic
AU  - Brun Luc
AU  - Josep Llados
AU  - Felipe Lumbreras
A2  - S+SSPR
PY  - 2016//
TI  - Information Theoretic Rotationwise Robust Binary Descriptor Learning
BT  - Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)
SP  - 368
EP  - 378
N2  - In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications.
UR  - http://dx.doi.org/10.1007/978-3-319-49055-7_33
N1  - DAG; ADAS; 600.097; 600.086
ID  - Youssef El Rhabi2016
ER  -