Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise
Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier. H...
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| Language: | en |
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PLos One
2025
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| Online Access: | http://eprints.uthm.edu.my/12673/1/J19412_26043a8b3d6d178ccfbb3179567f80d8.pdf http://eprints.uthm.edu.my/12673/ https://doi.org/10.1371/journal.pone.0315135 |
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| author | Tiwari, Shamik Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Abdul Aziz, Izzatdin Deepak Gupta, Deepak Gupta Jain, Antima Mahdin, Hairulnizam Senthil Athithan, Senthil Athithan Hidayat, Rahmat |
| author_facet | Tiwari, Shamik Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Abdul Aziz, Izzatdin Deepak Gupta, Deepak Gupta Jain, Antima Mahdin, Hairulnizam Senthil Athithan, Senthil Athithan Hidayat, Rahmat |
| author_sort | Tiwari, Shamik |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier. However, computing the performance of these methods in the presence of various degradations is always an open area of discussion. Image noise is
always a dominant factor among various image degradation factors, affecting the performance of these methods and making texture classification challenging. Therefore, it is essential to investigate the interpretation of these methods in the presence of prominent degradation factors such as noise. Applications for Segmentation-Based Fractal Texture Features (SFTF) include image classification, texture generation, and medical image analysis. They are beneficial for examining textures with intricate, erratic patterns that are difficult
to characterize using conventional statistical techniques accurately. This paper assesses two texture feature extraction methods based on SFTF and statistical moment-based texture features in the presence and absence of Gaussian noise. The SFTF and statistical moments-based handcrafted features are passed to a multilayer feed-forward neural network for classification. These models are evaluated on natural textures from Kylberg Texture Dataset 1.0. The results show the superiority of segmentation-based fractal analysis over other approaches. The average accuracy rates using the SFTF are 99% and 97% in the absence and presence of Gaussian noise, respectively |
| format | Article |
| id | my.uthm.eprints-12673 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2025 |
| publisher | PLos One |
| record_format | eprints |
| spelling | my.uthm.eprints-126732025-06-05T07:46:45Z http://eprints.uthm.edu.my/12673/ Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise Tiwari, Shamik Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Abdul Aziz, Izzatdin Deepak Gupta, Deepak Gupta Jain, Antima Mahdin, Hairulnizam Senthil Athithan, Senthil Athithan Hidayat, Rahmat TA Engineering (General). Civil engineering (General) Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier. However, computing the performance of these methods in the presence of various degradations is always an open area of discussion. Image noise is always a dominant factor among various image degradation factors, affecting the performance of these methods and making texture classification challenging. Therefore, it is essential to investigate the interpretation of these methods in the presence of prominent degradation factors such as noise. Applications for Segmentation-Based Fractal Texture Features (SFTF) include image classification, texture generation, and medical image analysis. They are beneficial for examining textures with intricate, erratic patterns that are difficult to characterize using conventional statistical techniques accurately. This paper assesses two texture feature extraction methods based on SFTF and statistical moment-based texture features in the presence and absence of Gaussian noise. The SFTF and statistical moments-based handcrafted features are passed to a multilayer feed-forward neural network for classification. These models are evaluated on natural textures from Kylberg Texture Dataset 1.0. The results show the superiority of segmentation-based fractal analysis over other approaches. The average accuracy rates using the SFTF are 99% and 97% in the absence and presence of Gaussian noise, respectively PLos One 2025 Article PeerReviewed text en http://eprints.uthm.edu.my/12673/1/J19412_26043a8b3d6d178ccfbb3179567f80d8.pdf Tiwari, Shamik and Akhilesh Kumar Sharma, Akhilesh Kumar Sharma and Abdul Aziz, Izzatdin and Deepak Gupta, Deepak Gupta and Jain, Antima and Mahdin, Hairulnizam and Senthil Athithan, Senthil Athithan and Hidayat, Rahmat (2025) Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise. Investigations on segmentation-based fractal texture. pp. 1-17. https://doi.org/10.1371/journal.pone.0315135 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Tiwari, Shamik Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Abdul Aziz, Izzatdin Deepak Gupta, Deepak Gupta Jain, Antima Mahdin, Hairulnizam Senthil Athithan, Senthil Athithan Hidayat, Rahmat Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise |
| title | Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise |
| title_full | Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise |
| title_fullStr | Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise |
| title_full_unstemmed | Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise |
| title_short | Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise |
| title_sort | investigations on segmentation-based fractal texture for texture classification in the presence of gaussian noise |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.uthm.edu.my/12673/1/J19412_26043a8b3d6d178ccfbb3179567f80d8.pdf http://eprints.uthm.edu.my/12673/ https://doi.org/10.1371/journal.pone.0315135 |
| url_provider | http://eprints.uthm.edu.my/ |