IEEE Trans Image Process - Manifold regularized multitask learning for semi-supervised multilabel image classification.

Tópicos

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Resumo

It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

Resumo Limpo

signific challeng classifi imag multipl label use small number label sampl one option learn binari classifi label use manifold regular improv classif perform explor under geometr structur data distribut howev approach perform well practic imag multipl concept repres highdimension visual featur thus manifold regular insuffici control model complex paper propos manifold regular multitask learn mrmtl algorithm mrmtl learn discrimin subspac share multipl classif task exploit common structur task effect control model complex differ task limit one anoth search volum manifold regular ensur function share hypothesi space smooth along data manifold conduct extens experi pascal voc dataset class mir dataset class compar mrmtl popular imag classif algorithm result suggest mrmtl effect imag classif

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