Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise
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| Publicado en: | PLoS One vol. 20, no. 1 (Jan 2025), p. e0315135 |
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| Otros Autores: | , , , , , , |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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|---|---|---|---|
| 001 | 3154102136 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0315135 |2 doi | |
| 035 | |a 3154102136 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Tiwari, Shamik | |
| 245 | 1 | |a Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise | |
| 260 | |b Public Library of Science |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Feature extraction | ||
| 653 | |a Deep learning | ||
| 653 | |a Fractal analysis | ||
| 653 | |a Multilayers | ||
| 653 | |a Degradation | ||
| 653 | |a Segmentation | ||
| 653 | |a Normal distribution | ||
| 653 | |a Medical imaging | ||
| 653 | |a Neural networks | ||
| 653 | |a Fractals | ||
| 653 | |a Decomposition | ||
| 653 | |a Image degradation | ||
| 653 | |a Image processing | ||
| 653 | |a Classification | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Statistical models | ||
| 653 | |a Fractal models | ||
| 653 | |a Image analysis | ||
| 653 | |a Image segmentation | ||
| 653 | |a Computer vision | ||
| 653 | |a Image retrieval | ||
| 653 | |a Retrieval | ||
| 653 | |a Image classification | ||
| 653 | |a Statistical methods | ||
| 653 | |a Random noise | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Texture | ||
| 653 | |a Information retrieval | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Sharma, Akhilesh Kumar | |
| 700 | 1 | |a Izzatdin Abdul Aziz | |
| 700 | 1 | |a Gupta, Deepak | |
| 700 | 1 | |a Jain, Antima | |
| 700 | 1 | |a Mahdin, Hairulnizam | |
| 700 | 1 | |a Athithan, Senthil | |
| 700 | 1 | |a Hidayat, Rahmat | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 1 (Jan 2025), p. e0315135 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3154102136/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3154102136/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3154102136/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |