Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2019.00519
Title: Deep Graphical Feature Learning for the Feature Matching Problem
Authors: Zhen Zhang
Wee Sun Lee 
Issue Date: 2019
Publisher: IEEE
Citation: Zhen Zhang, Wee Sun Lee (2019). Deep Graphical Feature Learning for the Feature Matching Problem. International Conference on Computer Vision (ICCV) : 5087-5095. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2019.00519
Abstract: The feature matching problem is a fundamental problem in various areas of computer vision including image registration, tracking and motion analysis. Rich local representation is a key part of efficient feature matching methods. However, when the local features are limited to the coordinate of key points, it becomes challenging to extract rich local representations. Traditional approaches use pairwise or higher order handcrafted geometric features to get robust matching; this requires solving NP-hard assignment problems. In this paper, we address this problem by proposing a graph neural network model to transform coordinates of feature points into local features. With our local features, the traditional NP-hard assignment problems are replaced with a simple assignment problem which can be solved efficiently. Promising results on both synthetic and real datasets demonstrate the effectiveness of the proposed method.
Source Title: International Conference on Computer Vision (ICCV)
URI: https://scholarbank.nus.edu.sg/handle/10635/187345
DOI: 10.1109/ICCV.2019.00519
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Zhang2019ICCV.pdf673.09 kBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.