Leveraging deep learning for data processing to improve discriminative modeling capabilities

Amir K. Saeed; Benjamin M. Rodriguez

doi:10.1117/12.3029851

7 June 2024 Leveraging deep learning for data processing to improve discriminative modeling capabilities

Amir K. Saeed, Benjamin M. Rodriguez

Proceedings Volume 13040, Pattern Recognition and Tracking XXXV; 130400B (2024) https://doi.org/10.1117/12.3029851
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Effective preprocessing of image data plays a pivotal role in enhancing the discriminative modeling capabilities in downstream machine learning tasks. This study investigates the significance of adequately mapping image data into a new feature space during the preprocessing phase, emphasizing its criticality in facilitating more robust and accurate models. While traditional methods such as signal/image processing transforms have been previously explored for this purpose, this study introduces a novel approach leveraging deep learning techniques. Specifically, convolutional and pooling layers are employed to process the image data, offering a more sophisticated and adaptive method for feature extraction and representation. By employing deep learning architectures, the preprocessing phase becomes more flexible and capable of capturing intricate patterns and structures within the data. Through empirical evaluation, our approach demonstrates significant improvements in discriminative modeling across various traditional machine learning approaches. This highlights the effectiveness and versatility of deep learning-based preprocessing in enhancing the performance of downstream tasks, showcasing its potential to advance the field of image data processing and analysis.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Amir K. Saeed and Benjamin M. Rodriguez "Leveraging deep learning for data processing to improve discriminative modeling capabilities", Proc. SPIE 13040, Pattern Recognition and Tracking XXXV, 130400B (7 June 2024); https://doi.org/10.1117/12.3029851

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