In this paper, a novel approach is developed to automatically annotate image content by a semi-supervised learning model. With perceptual visual characteristics ...
Semi-Supervised Learning Model Based Efficient. Image Annotation. Songhao Zhu*. School of Automation Engineering. Nanjing University of Post and ...
Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks.
Semi-supervised learning methods exist for dealing with a lack of labels, but they generally do not address the problem of class imbalance. Hence, the purpose ...
In this paper, a novel approach is developed to automatically annotate image content by a semi-supervised learning model. With perceptual visual characteristics ...
Jun 27, 2018 · This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic ...
Semi-supervised learning methods can efficiently reduce the annotation efforts by exploring the potential information hidden in unlabeled images, an area ...
Automatic image annotation is a promising way to achieve more effective image management and retrieval by using keywords. However, system performances of the ...
In this paper, we try to attack the problem based on 2D CRFs (Conditional Random Fields) and semi-supervised learning which are seamlessly integrated into a ...
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Mar 29, 2024 · Semi-supervised learning (SSL) is a machine learning technique that uses a small portion of labeled data and lots of unlabeled data to train a predictive model.