DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification
Compressive Sensing (CS) technique in image compression represents efficient signal which offering solutions in image classification where the resources are constrained especially on a large image processing, storage resource, and computing performance. Compressive learning (CL) is a framework that...
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2024-12-01
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| Series: | Journal of Applied Engineering and Technological Science |
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| Online Access: | https://journal.yrpipku.com/index.php/jaets/article/view/4506 |
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| author | Lutvi Murdiansyah Murdiansyah Gelar Budiman Indrarini Irawati Sugondo Hadiyoso A. V. Senthil Kumar |
| author_facet | Lutvi Murdiansyah Murdiansyah Gelar Budiman Indrarini Irawati Sugondo Hadiyoso A. V. Senthil Kumar |
| author_sort | Lutvi Murdiansyah Murdiansyah |
| collection | DOAJ |
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Compressive Sensing (CS) technique in image compression represents efficient signal which offering solutions in image classification where the resources are constrained especially on a large image processing, storage resource, and computing performance. Compressive learning (CL) is a framework that integrates signal acquisition via compressed sensing (CS) and machine/deep learning for inference tasks directly on a small number of measurements, On the other hand, in real-world high-resolution (HR) data, where the image dataset is very limited CL, has the drawback of reduced accuracy under conditions of aggressive compression ratio. Here, a reconstruction method is necessary to maintain high levels of accuracy. To address this, we proposed a framework Deep Learning (DL) and Compressive Sensing that processing a small dataset of 92 images maintaining high accuracy. The framework developed in this paper employs processing sensing matrix A in compressive sensing with two transformation methods: DCT CL with Multi Neural Networks and the SVD method with GoogleNet framework. To maintain the same computation efficiency as DCT Compressive learning, SVD with GoogleNet framework provides a solution for object recognition, achieving accuracy values ranging from 89.47% to 63.15% for compression ratios of 3.97 to 31.75. This performance shows a linear tendency concerning the PSNR level, an index of signal reconstruction quality, and demonstrates an efficient process in the S matrix.
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| format | Article |
| id | doaj-art-a6e80dce8cbb45f4bc85af1c23cbe339 |
| institution | OA Journals |
| issn | 2715-6087 2715-6079 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj-art-a6e80dce8cbb45f4bc85af1c23cbe3392025-08-20T01:58:03ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792024-12-016110.37385/jaets.v6i1.4506DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification Lutvi Murdiansyah Murdiansyah0Gelar Budiman1Indrarini Irawati2Sugondo Hadiyoso3A. V. Senthil Kumar4School of Electrical Engineering, Telkom UniversitySchool of Electrical Engineering, Telkom UniversitySchool of Applied Science, Telkom UniversitySchool of Applied Science, Telkom UniversityHindustan College of Arts and Science, Coimbatore, India Compressive Sensing (CS) technique in image compression represents efficient signal which offering solutions in image classification where the resources are constrained especially on a large image processing, storage resource, and computing performance. Compressive learning (CL) is a framework that integrates signal acquisition via compressed sensing (CS) and machine/deep learning for inference tasks directly on a small number of measurements, On the other hand, in real-world high-resolution (HR) data, where the image dataset is very limited CL, has the drawback of reduced accuracy under conditions of aggressive compression ratio. Here, a reconstruction method is necessary to maintain high levels of accuracy. To address this, we proposed a framework Deep Learning (DL) and Compressive Sensing that processing a small dataset of 92 images maintaining high accuracy. The framework developed in this paper employs processing sensing matrix A in compressive sensing with two transformation methods: DCT CL with Multi Neural Networks and the SVD method with GoogleNet framework. To maintain the same computation efficiency as DCT Compressive learning, SVD with GoogleNet framework provides a solution for object recognition, achieving accuracy values ranging from 89.47% to 63.15% for compression ratios of 3.97 to 31.75. This performance shows a linear tendency concerning the PSNR level, an index of signal reconstruction quality, and demonstrates an efficient process in the S matrix. https://journal.yrpipku.com/index.php/jaets/article/view/4506Compressive LearningAccuracyDiscrete Cosine TransformSingular Value DecompositionCompressive Sensing |
| spellingShingle | Lutvi Murdiansyah Murdiansyah Gelar Budiman Indrarini Irawati Sugondo Hadiyoso A. V. Senthil Kumar DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification Journal of Applied Engineering and Technological Science Compressive Learning Accuracy Discrete Cosine Transform Singular Value Decomposition Compressive Sensing |
| title | DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification |
| title_full | DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification |
| title_fullStr | DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification |
| title_full_unstemmed | DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification |
| title_short | DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification |
| title_sort | dct and svd sparsity based compressive learning on lettuces classification |
| topic | Compressive Learning Accuracy Discrete Cosine Transform Singular Value Decomposition Compressive Sensing |
| url | https://journal.yrpipku.com/index.php/jaets/article/view/4506 |
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