- Title
- Enhancing the effectiveness of local descriptor based image matching
- Creator
- Hossain, Md Tahmid; Teng, Shyh; Zhang, Dengsheng; Lim, Suryani; Lu, Guojun
- Date
- 2018
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/168562
- Identifier
- vital:13847
- Identifier
-
https://doi.org/10.1109/DICTA.2018.8615800
- Identifier
- ISBN:9781538666029 (ISBN)
- Abstract
- Image registration has received great attention from researchers over the last few decades. SIFT (Scale Invariant Feature Transform), a local descriptor-based technique is widely used for registering and matching images. To establish correspondences between images, SIFT uses a Euclidean Distance ratio metric. However, this approach leads to a lot of incorrect matches and eliminating these inaccurate matches has been a challenge. Various methods have been proposed attempting to mitigate this problem. In this paper, we propose a scale and orientation harmony-based pruning method that improves image matching process by successfully eliminating incorrect SIFT descriptor matches. Moreover, our technique can predict the image transformation parameters based on a novel adaptive clustering method with much higher matching accuracy. Our experimental results have shown that the proposed method has achieved averages of approximately 16% and 10% higher matching accuracy compared to the traditional SIFT and a contemporary method respectively.; 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-8
- Rights
- Copyright © 2018 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Euclidean distance; Image registration; K-means clustering; Key point; Local descriptor; Orientation difference; Scale ratio; SIFT
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