Dynamic Image Optimization and Code Generation Platform for Enhanced Data Augmentation
Creators
- 1. Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
- 1. Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Description
Abstract: In the rapidly evolving domain of machine learning, the critical role of data quality, particularly image data, cannot be overstated. This research introduces a novel system uniquely designed to significantly improve the preprocessing and augmentation of image data for machine learning applications. At its core, the platform emerges as a comprehensive solution, meticulously bridging the gap between the acquisition of raw image data and its transformation into an optimized form ready for machine learning algorithms. What has been discovered, is a multifaceted system that not only simplifies the enhancement of image data but also elevates the quality of machine learning models by providing access to advanced image optimization techniques. The system distinguishes itself through a highly intuitive user interface that guides users in selecting and applying a variety of optimization strategies. These strategies are meticulously designed to enhance image quality and diversity, which in turn, can significantly improve the performance of machine learning models trained with such data. The platform's backend, powered by Python and leveraging libraries such as OpenCV, Pillow, and scikit-image, coupled with a responsive front end, ensures a seamless user experience and high-quality image processing. The generation of Python code for each processed image is a distinctive feature that enhances the platform's educational value, allowing users to learn, customize, and integrate optimization techniques into their workflows. Moreover, the inclusion of an API extends the platform's utility beyond its web interface, facilitating the automation of dataaugmentation pipelines and integration with external applications. This platform not only meets the immediate needs of data scientists and machine learning practitioners for data preprocessing and augmentation but also contributes significantly to the field by promoting understanding and application of image optimization techniques.
Files
D443213040424.pdf
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Additional details
Identifiers
- DOI
- 10.35940/ijeat.D4432.13040424
- EISSN
- 2249-8958
Dates
- Accepted
-
2024-04-15Manuscript received on 26 March 2024 | Revised Manuscript received on 03 April 2024 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 April 2024.
References
- M.J.Smith, "Complexity and Ease of Use: A Design Study", 2006, DOI:10.4017/gt.2006.05.02.008.00 https://doi.org/10.4017/gt.2006.05.02.008.00
- Sylvain Paris, Samuel W. Hasinoff, Jan Kautz, "Local Laplacian filters: edge-aware image processing with a Laplacian pyramid" 2015 IEEE, DOI: https://doi.org/10.1145/2723694
- Wencheng Wang; Xiaohui Yuan; Xiaojin Wu; Yunlong Liu, "Fast Image Dehazing Method Based on Linear Transformation", 2017 IEEE, DOI: https://doi.org/10.1109/TMM.2017.2652069
- Dávila Guzmán, M.A., Nozal, R., Gran Tejero, R. et al., "Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL", 2019, DOI - https://doi.org/10.1007/s11227-019-02768-y
- Seokjae Lim; Wonjun Kim, "DSLR: Deep Stacked Laplacian Restorer for Low-Light Image Enhancement". 2020 IEEE, DOI: https://doi.org/10.1109/TMM.2020.3039361
- Zhen Li, Ying Jiang, The Automatic Code Generation, 2021, DOI: 10.1016/j. procs.2020.02.099
- Massimo Salvi a, U. Rajendra Acharya b c d, Filippo Molinari a, Kristen M. Meiburger, The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis, 2021 ELESVIER, https://doi.org/10.1016/j.compbiomed.2020.104129
- Anu V Kottath, SV Shri Bharathi, "Image Preprocessing Techniques in Skin Diseases Prediction Using Deep Learning: A Review". 2022 IEEE, DOI: https://doi.org/10.1109/ICIRCA54612.2022.9985547
- Heriyanni, E., & Darudiato, S. (2020). Improving Quality of Academic Advisory Information with Executive Information System. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 5, pp. 1737–1743). https://doi.org/10.35940/ijrte.e6233.018520
- Anshu, K., Gaur, L., & Agarwal, V. (2019). Evaluating Niche E-commerce Indian Retail Websites: User Perspective. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 9, pp. 780– 789). https://doi.org/10.35940/ijitee.h7008.078919
- Singh, N., & Panda, S. P. (2020). Stimulating Deep Learning Network on Graphical Processing Unit to Predict Water Level. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 4, pp. 1222–1229). https://doi.org/10.35940/ijeat.d8452.049420
- A., O., & O, B. (2020). An Iris Recognition and Detection System Implementation. In International Journal of Inventive Engineering and Sciences (Vol. 5, Issue 8, pp. 8–10). https://doi.org/10.35940/ijies.h0958.025820
- . Sharma, D., & Sharma, Dr. P. (2023). Pre-Processing and Normalization of the Historical Weather Data Collected from Secondary Data Source for Rainfall Prediction. In Indian Journal of Mining (Vol. 3, Issue 2, pp. 11–14). https://doi.org/10.54105/ijdm.b1629.113223