Abstract
Over the past several decades camera technology has exploded in terms of higher quality, decreased cost, and increased availability. This means that there are significantly more images and videos being collected, which has developed an increased need for machine learning algorithms and methodologies that can extract and leverage relevant information from them. While much work has been done, current algorithms fall short on being able to both locate and classify objects in highly cluttered images, where it is difficult for even a human to identify objects. In this paper, we create and test a novel methodology to perform object detection in highly cluttered images by utilizing the partial least squares algorithm for dimensionality reduction, transfer learning for feature extraction, a newly developed object localization technique, and an ensemble of Extreme Learning Machines for classification. This methodology outperforms the current state of the art, Google’s AutoML Vision.
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Carolus Khan, K., Ratner, E., Douglas, C., Lendasse, A. (2023). A Novel Methodology for Object Detection in Highly Cluttered Images. In: Björk, KM. (eds) Proceedings of ELM 2021. ELM 2021. Proceedings in Adaptation, Learning and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-031-21678-7_2
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