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Volume 8, Issue 4
Adjacent Local Binary Patterns Based on Color Space Fusion for Color Image Classification

Shengsheng Wang, Dezhi Huang, Haiyang Jia, Dong Li & Bolou Bolou Dickson

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 783-790.

Published online: 2015-08

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  • Abstract
In this paper, we propose an improved image feature descriptor based on Local Binary Pattern, which is called Adjacent Local Binary Patterns based on Color Space Fusion (ALBPCSF). The proposed method fuses color feature and spatial relations. ALBPCSF uses the channel values of RGB and HSV color spaces to calculate the color feature. Then the proposed method considers the spatial relations which will be combined with the color feature. Finally, an image classification system framework based on ALBPCSF is given. In order to validate the performance, our method is compared with previous methods on Corel 1000 and MIT Vision Texture datasets. The results show that our approach is superior than other methods in color image classification.
  • Keywords

Local Binary Pattern Color Texture KNN Image Classification

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COPYRIGHT: © Global Science Press

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@Article{JFBI-8-783, author = {}, title = {Adjacent Local Binary Patterns Based on Color Space Fusion for Color Image Classification}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {4}, pages = {783--790}, abstract = {In this paper, we propose an improved image feature descriptor based on Local Binary Pattern, which is called Adjacent Local Binary Patterns based on Color Space Fusion (ALBPCSF). The proposed method fuses color feature and spatial relations. ALBPCSF uses the channel values of RGB and HSV color spaces to calculate the color feature. Then the proposed method considers the spatial relations which will be combined with the color feature. Finally, an image classification system framework based on ALBPCSF is given. In order to validate the performance, our method is compared with previous methods on Corel 1000 and MIT Vision Texture datasets. The results show that our approach is superior than other methods in color image classification.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00188}, url = {http://global-sci.org/intro/article_detail/jfbi/4760.html} }
TY - JOUR T1 - Adjacent Local Binary Patterns Based on Color Space Fusion for Color Image Classification JO - Journal of Fiber Bioengineering and Informatics VL - 4 SP - 783 EP - 790 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00188 UR - https://global-sci.org/intro/article_detail/jfbi/4760.html KW - Local Binary Pattern KW - Color Texture KW - KNN KW - Image Classification AB - In this paper, we propose an improved image feature descriptor based on Local Binary Pattern, which is called Adjacent Local Binary Patterns based on Color Space Fusion (ALBPCSF). The proposed method fuses color feature and spatial relations. ALBPCSF uses the channel values of RGB and HSV color spaces to calculate the color feature. Then the proposed method considers the spatial relations which will be combined with the color feature. Finally, an image classification system framework based on ALBPCSF is given. In order to validate the performance, our method is compared with previous methods on Corel 1000 and MIT Vision Texture datasets. The results show that our approach is superior than other methods in color image classification.
Shengsheng Wang, Dezhi Huang, Haiyang Jia, Dong Li & Bolou Bolou Dickson. (2019). Adjacent Local Binary Patterns Based on Color Space Fusion for Color Image Classification. Journal of Fiber Bioengineering and Informatics. 8 (4). 783-790. doi:10.3993/jfbim00188
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