Dec 3, 2011 · This paper proposes an approach, called stable multi-label boosting with structural feature selection (S-MtBFS), for image annotation. S-MtBFS ...
This paper proposes an approach, called stable multi-label boosting with structural feature selection (S-MtBFS), for image annotation. S-MtBFS comprises two ...
Abstract Automatic annotating images with appropriate multiple tags are very important to image retrieval and image understanding.
We call it Multi-label Image Boosting by the selection of heterogeneous features with structural Grouping Sparsity (MtBGS). MtBGS induces a (structural) sparse ...
Figure 1: Flowchart of the heterogeneous feature selection with structural grouping sparsity. This paper is interested in seeking after an interpretable.
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In this paper, Markov blanket (MB) is chosen, which is amenable to represent the local causal structure of a variable and has been used in single label causal ...
Jan 24, 2024 · Abstract—Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point. In.
The task of multi-label feature selection is to select a feature subset containing informative features with respect to the label set [9], [10], [11], [12].
[Schapire and Singer, 2000]; in image annotation, an image needs to be ... the increasing of the number of selected features, the perfor- mance of all ...