- Title
- A novel fusion approach in the extraction of kernel descriptor with improved effectiveness and efficiency
- Creator
- Karmakar, Priyabrata; Teng, Shyh; Lu, Guojun; Zhang, Dengsheng
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180935
- Identifier
- vital:15865
- Identifier
-
https://doi.org/10.1007/s11042-020-10300-1
- Identifier
- ISBN:1380-7501
- Abstract
- Image representation using feature descriptors is crucial. A number of histogram-based descriptors are widely used for this purpose. However, histogram-based descriptors have certain limitations and kernel descriptors (KDES) are proven to overcome them. Moreover, the combination of more than one KDES performs better than an individual KDES. Conventionally, KDES fusion is performed by concatenating them after the gradient, colour and shape descriptors have been extracted. This approach has limitations in regard to the efficiency as well as the effectiveness. In this paper, we propose a novel approach to fuse different image features before the descriptor extraction, resulting in a compact descriptor which is efficient and effective. In addition, we have investigated the effect on the proposed descriptor when texture-based features are fused along with the conventionally used features. Our proposed descriptor is examined on two publicly available image databases and shown to provide outstanding performances.
- Relation
- Multimedia Tools and Applications Vol. 80, no. 10 (Apr 2021), p. 14545-14564
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021
- Subject
- 4603 Computer Vision and Multimedia Computation; 4606 Distributed Computing and Systems Software
- Full Text
- Reviewed
- Funder
- This research was partially supported by Australian Research Council Discovery Projects scheme: DP130100024.
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