Bagging-based cross-media retrieval algorithm

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 5; pp. 2615 - 2623
• Main Authors: Xu, Gongwen, Zhang, Yu, Yin, Mingshan, Hong, Wenzhong, Zou, Ran, Wang, Shanshan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Data Analytics and Machine Learning; Engineering; Mathematical Logic and Foundations; Mechatronics; Robotics; Ensemble learning; Cross-media retrieval; Bagging
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-022-07587-7

It is hard to come up with a strong learning algorithm with high cross-media retrieval accuracy, but finding a weak learning algorithm with slightly higher accuracy than random prediction is simple. This paper proposes an innovative Bagging-based cross-media retrieval algorithm (called BCMR) based on this concept. First, we use bootstrap sampling to take a random sample from the original set. The amount of sample abstracted by bootstrapping is set to be the same as the amount of sample abstracted by the original dataset. Secondly, 50 bootstrap replicates are used to train 50 weak classifiers. In our experiments, we used homogeneous individual classifiers and eight different baseline methods. Last but not least, we generate the final strong classifier from 50 weak classifiers using the sample voting integration strategy. Using collective wisdom, we can get rid of bad decisions, giving the integrated model a much better generalization ability. Extensive experiments on three datasets show that BCMR can significantly improve cross-media retrieval accuracy.