Bag of Compact HKS-based Feature Descriptors

ElNaghy, Hanan; Hamad, Safwat

Bag of Compact HKS-based Feature Descriptors

Files

087-090.pdf (30.93 MB)

Date

2015

Authors

ElNaghy, Hanan
Hamad, Safwat

Publisher

The Eurographics Association

Abstract

3D object retrieval has become an integral part in many today's applications attracting extensive research efforts. This paper introduces an enhanced 3D object retrieval technique using a compact and highly discriminative feature point descriptor. The key idea is based on integrating Bag of features (BoF) paradigm with Heat Kernel Signature (HKS) for feature description and detection. Initially, HKS computation phase defines HKS point signatures for each 3D model. Then, an innovative feature point detection algorithm provides a succinct set of feature points to be associated with a compact HKS-based descriptor vectors computed at local time scales. Finally, we take advantage of the BoF paradigm to encode a given 3D model with an informative feature frequency vector. The proposed approach has been evaluated on SHREC 2015 dataset of non-rigid models. The experimental results demonstrate the effective retrieval performance, invariance to different kinds of deformation and possible noise.

        @inproceedings{10.2312:3dor.20151060
,
booktitle = {Eurographics Workshop on 3D Object Retrieval
},
editor = {I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
},
title = {{Bag of Compact HKS-based Feature Descriptors
}},
author = {ElNaghy, Hanan and 
Hamad, Safwat
},
year = {2015
},
publisher = {The Eurographics Association
},
ISBN = {},
DOI = {10.2312/3dor.20151060
}
}