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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315135 |
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| author | Shamik Tiwari Akhilesh Kumar Sharma Izzatdin Abdul Aziz Deepak Gupta Antima Jain Hairulnizam Mahdin Senthil Athithan Rahmat Hidayat |
| author_facet | Shamik Tiwari Akhilesh Kumar Sharma Izzatdin Abdul Aziz Deepak Gupta Antima Jain Hairulnizam Mahdin Senthil Athithan Rahmat Hidayat |
| author_sort | Shamik Tiwari |
| collection | DOAJ |
| 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 | doaj-art-cefb954178ea4b7fa01b48a1468e6872 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-cefb954178ea4b7fa01b48a1468e68722025-01-17T05:31:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031513510.1371/journal.pone.0315135Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise.Shamik TiwariAkhilesh Kumar SharmaIzzatdin Abdul AzizDeepak GuptaAntima JainHairulnizam MahdinSenthil AthithanRahmat HidayatTexture 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.https://doi.org/10.1371/journal.pone.0315135 |
| spellingShingle | Shamik Tiwari Akhilesh Kumar Sharma Izzatdin Abdul Aziz Deepak Gupta Antima Jain Hairulnizam Mahdin Senthil Athithan Rahmat Hidayat Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0315135 |
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