Dynamic Image Optimization and Code Generation Platform for Enhanced Data Augmentation
Saivijaay V K1, S. Ganesh Kumar2
1Saivijaay V K , 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.
2Dr. S. Ganesh Kumar, 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.
Manuscript received on 26 March 2024 | Revised Manuscript received on 03 April 2024 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 April 2024 | PP: 23-28 | Volume-13 Issue-4, April 2024 | Retrieval Number: 100.1/ijeat.D443213040424 | DOI: 10.35940/ijeat.D4432.13040424
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 data augmentation 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.
Keywords: Optimization, Codeless Framework, Image Preprocessing, Automation, Data Preprocessing
Scope of the Article: Image Processing and Pattern Recognition