- Title
- Improved Tamura features for image classification using kernel based descriptors
- Creator
- Karmakar, Priyabrata; Teng, Shyh; Zhang, Dengsheng; Liu, Ying; Lu, Guojun
- Date
- 2017
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165195
- Identifier
- vital:13234
- Identifier
-
https://doi.org/10.1109/DICTA.2017.8227447
- Identifier
- ISBN:978-1-5386-2839-3
- Abstract
- Tamura features are based on human visual perception and have huge potential in image representation. Conventional Tamura features only work on homogeneous texture images and perform poor on generic images. Therefore, many researchers attempt to improve Tamura features and most of the improvements are based on histogram based representation. Kernel descriptors have been shown to outperform existing histogram based local features as such descriptors do not require coarse quantization of pixel attributes. Instead, in kernel descriptor framework, each pixel equally participates in matching between two image patches. In this paper, we propose a set of kernel descriptors that are based on Tamura features. Additionally, the proposed descriptors are invariant to local rotations. Experimental results show that our proposed approach outperforms the conventional Tamura features significantly.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA); Sydney, Australia; 29th November-1st December 2017 p. 461-467
- Rights
- Copyright © 2017 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Tamura features; Kernel descriptor; Rotation invariance; Image classification
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