Research on images multi-objective classification method based on attention mechanism

Biliu Wang

doi:10.1117/12.3029359

22 May 2024 Research on images multi-objective classification method based on attention mechanism

Biliu Wang

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760R (2024) https://doi.org/10.1117/12.3029359
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

At present, image processing has become an important method in tremendous downstream applications including unmanned driving, facial recognition, medical detection, etc. However, existing processing methods are concentrated on single targets and leak the ability of disposing multiple objectives especially in complex environment. As far as we can concern, current methods have unacceptable classification accuracy in multiple objectives scenario due to the confounding influence from similar pixels information. In this work, we propose a novel framework to identify multiple targets by extracting features from feature space and not rely on the pixel difference information. Initially, we utilize a supervised contrastive learning (S-CTL) to encourage the feature representation of input images. Subsequently, multiple convolutional neural networks are trained to classify the labels of multiple features with attention mechanism. From our extensive experiment analysis and results, we can significantly observe that our model can outperform existing multi-objectives classification methods with satisfied accuracy and reasonable computation costs.

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

Citation Download Citation

Biliu Wang "Research on images multi-objective classification method based on attention mechanism", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760R (22 May 2024); https://doi.org/10.1117/12.3029359

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KEYWORDS

Education and training

Machine learning

Image classification

Object detection

Visual process modeling

Data modeling

Detection and tracking algorithms

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