Multi label image classification learning with weak labels

Shan Lu; Quansheng Dou

doi:10.1117/12.2685646

19 October 2023 Multi label image classification learning with weak labels

Shan Lu, Quansheng Dou

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127094K (2023) https://doi.org/10.1117/12.2685646
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

A new multi-label similarity semantic learning(ML-SSL) model was proposed to solve the problem of label missing in existing multi-label image classification methods, It can produce better classification results by effectively recovering missing label information in training data. The model considers the characteristics of label structure and instance features, and recovers the missing label information in training data by using the label correlation within images and the similarity between images. After label recovery, the new training set is used to train the classification model, and the model is used to predict the test set. The experimental results show that the model has better performance improvement in image classification tasks under weak labeling.

Citation Download Citation

Shan Lu and Quansheng Dou "Multi label image classification learning with weak labels", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127094K (19 October 2023); https://doi.org/10.1117/12.2685646

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