Multi‐label enhancement manifold learning algorithm for vehicle video

• Published in: Concurrency and computation Vol. 35; no. 13
• Main Authors: Tan, Chao, Ji, Genlin, Zeng, Xiaoqian
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 10.06.2023
• Subjects: Algorithms; Classification; Feature extraction; label distribution learning; Machine learning; Manifolds (mathematics); Maximum entropy; multi‐label enhancement manifold learning; Performance prediction; Topology; vehicle video; Video data
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6660

In this article, we propose a new multi‐label enhancement manifold learning algorithm to solve the vehicle video classification problem. Predicting multiple objects in a traffic video image is a challenging problem. Traditional multi‐label classification methods can solve the problem of simultaneous detection of multiple labels, but cannot handle high‐dimensional streaming video data. Our idea is to use label distribution learning (LDL) to enrich the label space and improve label recognition in the original label space. We use the feature function representing the manifold structure to guide the geometric meaning of the label space and transform the local topology from the feature space to the label space. We first build a label distribution learner. Next, use the LDL model for classification. The similarity between the two distributions is measured by Bayesian divergence, and the label distribution is learned through the maximum entropy model and the objective function of this article is established. Finally, an enhanced label model of the manifold space is established to reduce the dimensionality of the feature matrix generated during the training phase, so that the supervised information in the label manifold can be used in the incremental manifold space to improve the accuracy of feature extraction. Compared to the latest multi‐label learning methods, our multi‐label enhancement manifold learning method has advantages in predicting performance.