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A novel fusion approach in the extraction of kernel descriptor with improved effectiveness and efficiency

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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.

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Acknowledgements

This research was partially supported by Australian Research Council Discovery Projects scheme: DP130100024.

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Authors and Affiliations

  1. School of Engineering, IT and Physical Sciences, Federation University Australia, Gippsland Campus, Churchill, VIC-3842, Australia

    Priyabrata Karmakar, Shyh Wei Teng, Guojun Lu & Dengsheng Zhang

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  1. Priyabrata Karmakar
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  2. Shyh Wei Teng
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  3. Guojun Lu
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  4. Dengsheng Zhang
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Correspondence to Priyabrata Karmakar.

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Karmakar, P., Teng, S.W., Lu, G. et al. A novel fusion approach in the extraction of kernel descriptor with improved effectiveness and efficiency. Multimed Tools Appl 80, 14545–14564 (2021). https://doi.org/10.1007/s11042-020-10300-1

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  • DOI: https://doi.org/10.1007/s11042-020-10300-1

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