LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification
Classifying texture images, especially those with significant rotation, illumination, scale, and viewpoint changes, is a fundamental and challenging problem in computer vision. This paper proposes a simple yet effective image descriptor, called Locally Encoded TRansform feature hISTogram (LETRIST),...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/142172 |
| _version_ | 1811687144072151040 |
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| author | Song, Tiecheng Li, Hongliang Meng, Fanman Wu, Qingbo Cai, Jianfei |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Song, Tiecheng Li, Hongliang Meng, Fanman Wu, Qingbo Cai, Jianfei |
| author_sort | Song, Tiecheng |
| collection | NTU |
| description | Classifying texture images, especially those with significant rotation, illumination, scale, and viewpoint changes, is a fundamental and challenging problem in computer vision. This paper proposes a simple yet effective image descriptor, called Locally Encoded TRansform feature hISTogram (LETRIST), for texture classification. LETRIST is a histogram representation that explicitly encodes the joint information within an image across feature and scale spaces. The proposed representation is training-free, low-dimensional, yet discriminative and robust for texture description. It consists of the following major steps. First, a set of transform features is constructed to characterize local texture structures and their correlation by applying linear and non-linear operators on the extremum responses of directional Gaussian derivative filters in scale space. Established on the basis of steerable filters, the constructed transform features are exactly rotationally invariant as well as computationally efficient. Second, the scalar quantization via binary or multi-level thresholding is adopted to quantize these transform features into texture codes. Two quantization schemes are designed, both of which are robust to image rotation and illumination changes. Third, the cross-scale joint coding is explored to aggregate the discrete texture codes into a compact histogram representation, i.e., LETRIST. Experimental results on the Outex, CUReT, KTH-TIPS, and UIUC texture data sets show that LETRIST consistently produces better or comparable classification results than the state-of-the-art approaches. Impressively, recognition rates of 100.00% and 99.00% have been achieved on the Outex and KTH-TIPS data sets, respectively. In addition, the noise robustness is evaluated on the Outex and CUReT data sets. The source code is publicly available at https://github.com/stc-cqupt/letrist. |
| first_indexed | 2024-10-01T05:11:38Z |
| format | Journal Article |
| id | ntu-10356/142172 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:11:38Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1421722020-06-16T09:16:55Z LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification Song, Tiecheng Li, Hongliang Meng, Fanman Wu, Qingbo Cai, Jianfei School of Computer Science and Engineering School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Texture Classification Texture Analysis Classifying texture images, especially those with significant rotation, illumination, scale, and viewpoint changes, is a fundamental and challenging problem in computer vision. This paper proposes a simple yet effective image descriptor, called Locally Encoded TRansform feature hISTogram (LETRIST), for texture classification. LETRIST is a histogram representation that explicitly encodes the joint information within an image across feature and scale spaces. The proposed representation is training-free, low-dimensional, yet discriminative and robust for texture description. It consists of the following major steps. First, a set of transform features is constructed to characterize local texture structures and their correlation by applying linear and non-linear operators on the extremum responses of directional Gaussian derivative filters in scale space. Established on the basis of steerable filters, the constructed transform features are exactly rotationally invariant as well as computationally efficient. Second, the scalar quantization via binary or multi-level thresholding is adopted to quantize these transform features into texture codes. Two quantization schemes are designed, both of which are robust to image rotation and illumination changes. Third, the cross-scale joint coding is explored to aggregate the discrete texture codes into a compact histogram representation, i.e., LETRIST. Experimental results on the Outex, CUReT, KTH-TIPS, and UIUC texture data sets show that LETRIST consistently produces better or comparable classification results than the state-of-the-art approaches. Impressively, recognition rates of 100.00% and 99.00% have been achieved on the Outex and KTH-TIPS data sets, respectively. In addition, the noise robustness is evaluated on the Outex and CUReT data sets. The source code is publicly available at https://github.com/stc-cqupt/letrist. 2020-06-16T09:16:55Z 2020-06-16T09:16:55Z 2017 Journal Article Song, T., Li, H., Meng, F., Wu, Q., & Cai, J. (2018). LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification. IEEE Transactions on Circuits and Systems for Video Technology, 28(7), 1565-1579. doi:10.1109/tcsvt.2017.2671899 1051-8215 https://hdl.handle.net/10356/142172 10.1109/TCSVT.2017.2671899 2-s2.0-85049411257 7 28 1565 1579 en IEEE Transactions on Circuits and Systems for Video Technology © 2017 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Texture Classification Texture Analysis Song, Tiecheng Li, Hongliang Meng, Fanman Wu, Qingbo Cai, Jianfei LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification |
| title | LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification |
| title_full | LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification |
| title_fullStr | LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification |
| title_full_unstemmed | LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification |
| title_short | LETRIST : locally encoded transform feature histogram for rotation-invariant texture classification |
| title_sort | letrist locally encoded transform feature histogram for rotation invariant texture classification |
| topic | Engineering::Electrical and electronic engineering Texture Classification Texture Analysis |
| url | https://hdl.handle.net/10356/142172 |
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