Authors:
Yuan Liu
and
J. Paul Siebert
Affiliation:
University of Glasgow, United Kingdom
Keyword(s):
Binary Descriptor, Hexagonal Structure, Hierarchical Grouping, Local Feature Matching, Pose Estimate.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
In this paper, two new rotationally invariant hexagon-based binary descriptors (HBD), i.e., HexIDB and
HexLDB, are proposed in order to obtain better feature discriminability while encoding less redundant information.
Our new descriptors are generated based on a hexagonal grouping structure that improves upon the
HexBinary descriptor we reported previously. The third level descriptors of HexIDB and HexLDB have 270
bits and 99 bits respectively fewer than that of SHexBinary, due to sampling 61% fewer fields. Using learned
parameters, HBD demonstrates better performance when matching the majority of the images in Mikolajczyk
and Scmidt’s standard benchmark dataset, as compared to existing benchmark descriptors. Moreover, HBD
also achieves promising level of performance when applied to pose estimation using the ALOI dataset, achieving
0.5 pixels mean pose error, only slightly inferior to fixed-scale SIFT, but around 1.5 pixels better than
standard SIFT.