- Title
- A kernel-based approach for content-based image retrieval
- Creator
- Karmakar, Priyabrata; Teng, Shyh; Lu, Guojun; Zhang, Dengsheng
- Date
- 2018
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/169300
- Identifier
- vital:13977
- Identifier
-
https://doi.org/10.1109/IVCNZ.2018.8634760
- Identifier
- ISBN:2151-2191 (ISSN) 978-1-7281-0125-5 (ISBN)
- Abstract
- Content-based image retrieval (CBIR) is a popular approach to retrieve images based on a query. In CBIR, retrieval is executed based on the properties of image contents (e.g. gradient, shape, color, texture) which are generally encoded into image descriptors. Among the various image descriptors, histogram-based descriptors are very popular. However, they suffer from the limitation of coarse quantization. In contrast, the use of kernel descriptors (KDES) is proven to be more effective than histogram-based descriptors in other applications, e.g. image classification. This is because, in the KDES framework, instead of the quantization of pixel attributes, each pixel equally takes part in the similarity measurement between two images. In this paper, we propose an approach for how the conventional KDES and its improved version can be used for CBIR. In addition, we have provided a detailed insight into the effectiveness of improved kernel descriptors. Finally, our experiment results will show that kernel descriptors are significantly more effective than histogram-based descriptors in CBIR.
- Publisher
- Ieee
- Relation
- 2018 International Conference on Image and Vision Computing New Zealand; Auckland, New Zealand; 19th-21st November 2018 p. 1-6
- Rights
- Copyright © 2018 IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- Image retrieval; Kernel descriptor; Noise tolerance
- Reviewed
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