- Title
- An Enhanced Local Texture Descriptor for Image Segmentation
- Creator
- Tania, Sheikh; Murshed, Manzur; Teng, Shyh; Karmakar, Gour
- Date
- 2020
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177151
- Identifier
- vital:15239
- Identifier
-
https://doi.org/10.1109/ICIP40778.2020.9190895
- Identifier
- ISBN:1522-4880 (ISSN); 9781728163956 (ISBN)
- Abstract
- Texture is an indispensable property to develop many vision based autonomous applications. Compared to colour, feature dimension in a local texture descriptor is quite large as dense texture features need to represent the distribution of pixel intensities in the neighbourhood of each pixel. Large dimensional features require additional time for further processing that often restrict real-time applications. In this paper, a robust local texture descriptor is enhanced by reducing feature dimension by three folds without compromising the accuracy in region-based image segmentation applications. Reduction in feature dimension is achieved by exploiting the mean of neighbourhood pixel intensities radially along lines across a certain radius, which eliminates the need for sampling intensity distribution at three scales. Both the results of benchmark metrics and computational time are promising when the enhanced texture feature is used in a region-based hierarchical segmentation algorithm, a recent state-of-the-art technique. © 2020 IEEE.
- Publisher
- IEEE Computer Society
- Relation
- 2020 IEEE International Conference on Image Processing, ICIP 2020 Vol. 2020-October, p. 1526-1530
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2020 IEEE
- Subject
- Image segmentation; Local descriptor; Pattern recognition; Texture
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