C4.5 ALGORITHM BASED ADVERSARIAL LEARNING-BASED ADA BASED COLOR AND MULTISPECTRAL PROCESSING FOR ENHANCED IMAGE ANALYSIS
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| Title: | C4.5 ALGORITHM BASED ADVERSARIAL LEARNING-BASED ADA BASED COLOR AND MULTISPECTRAL PROCESSING FOR ENHANCED IMAGE ANALYSIS |
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| Authors: | Ananthi N, Thiyam Ibungomacha Singh, Nihar Ranjan Behera, Gnanamurthy R.K. |
| Source: | ICTACT Journal on Image and Video Processing. 14:3049-3054 |
| Publisher Information: | ICT Academy, 2023. |
| Publication Year: | 2023 |
| Description: | This research presents a novel approach that combines the C4.5 algorithm with Adversarial Learning-based Adaptive Data Augmentation (ADA) for Color and Multispectral Processing, leading to a significant enhancement in Image Analysis. The C4.5 algorithm, known for its decision tree construction, is integrated with ADA, which employs adversarial learning principles to generate diverse and realistic training samples. This integration enables the augmentation of both color and multispectral images, effectively boosting the robustness and accuracy of image analysis tasks. The proposed method showcases improved performance in various applications such as object recognition, classification, and scene understanding. Experimental results demonstrate the superiority of the proposed approach compared to traditional methods, substantiating its potential for advancing image analysis techniques. |
| Document Type: | Article |
| ISSN: | 0976-9102 0976-9099 |
| DOI: | 10.21917/ijivp.2023.0433 |
| Accession Number: | edsair.doi...........d4e41f7c5b1815fac6f9a30004c26315 |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | This research presents a novel approach that combines the C4.5 algorithm with Adversarial Learning-based Adaptive Data Augmentation (ADA) for Color and Multispectral Processing, leading to a significant enhancement in Image Analysis. The C4.5 algorithm, known for its decision tree construction, is integrated with ADA, which employs adversarial learning principles to generate diverse and realistic training samples. This integration enables the augmentation of both color and multispectral images, effectively boosting the robustness and accuracy of image analysis tasks. The proposed method showcases improved performance in various applications such as object recognition, classification, and scene understanding. Experimental results demonstrate the superiority of the proposed approach compared to traditional methods, substantiating its potential for advancing image analysis techniques. |
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| ISSN: | 09769102 09769099 |
| DOI: | 10.21917/ijivp.2023.0433 |