<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The local descriptors based on a binary pattern feature have state-of-the-art distinctiveness. However, their high dimensionality resists them from matching faster and being used in a low-end device. In this paper we propose an efficient and feasible learning method to select discriminative binary patterns for constructing a compact local descriptor. In the selection, a searching tree with Branch&amp;Bound is used instead of the exhaustive enumeration, in order to avoid tremendous computation in training. New local descriptors are constructed based on the selected patterns. The efficiency of selecting binary patterns has been confirmed by the evaluation of these new local descriptors’ performance in experiments of image matching and object recognition.</p></abstract>

The local descriptors based on a binary pattern feature have state-of-the-art distinctiveness. However, their high dimensionality resists them from matching faster and being used in a low-end device. In this paper we propose an efficient and feasible learning method to select discriminative binary patterns for constructing a compact local descriptor. In the selection, a searching tree with Branch&Bound is used instead of the exhaustive enumeration, in order to avoid tremendous computation in training. New local descriptors are constructed based on the selected patterns. The efficiency of selecting binary patterns has been confirmed by the evaluation of these new local descriptors’ performance in experiments of image matching and object recognition.

Selecting Discriminative Binary Patterns for a Local Feature

School of Computer and Information, Hefei University of Technology, Hefei 230009 Anhui, China

School of Electronics and Information, Anhui Jianzhu University, Hefei 230009 Anhui, China

School of Mathematics, Hefei University of Technology, Hefei 230009 Anhui, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Selecting Discriminative Binary Patterns for a Local Feature"}

The local descriptors based on a binary pattern feature have state-of-the-art distinctiveness. However, their high dimensionality resists them from matching...

Selecting Discriminative Binary Patterns for a Local Feature

Publié en ligne: 05 oct. 2015

Pages: 104 - 113

DOI: https://doi.org/10.1515/cait-2015-0044

Mots clés
Selecting patterns, searching tree, local descriptor, matching, binary pattern

© Yingying Li et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Selecting Discriminative Binary Patterns for a Local Feature

Publié en ligne: 05 oct. 2015

Pages: 104 - 113

DOI: https://doi.org/10.1515/cait-2015-0044

Mots clésSelecting patterns, searching tree, local descriptor, matching, binary pattern

© Yingying Li et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Selecting patterns, searching tree, local descriptor, matching, binary pattern