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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2024-12-01
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| Series: | Journal of Applied Engineering and Technological Science |
| Subjects: | |
| Online Access: | https://journal.yrpipku.com/index.php/jaets/article/view/4506 |
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| Summary: | 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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| ISSN: | 2715-6087 2715-6079 |