Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification

作者：Luo, Yong^*; Tao, Dacheng; Xu, Chang; Xu, Chao; Liu, Hong; Wen, Yonggang

来源：IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(5): 709-722.

DOI：10.1109/TNNLS.2013.2238682

• Image classification
• manifold
• multilabel
• multiview
• semisupervised

In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e. g., pedestrian, bicycle, and tree) and is properly characterized by multiple visual features (e. g., color, texture, and shape). Currently, available tools ignore either the label relationship or the view complementarily. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multilabel structure in the output space, we introduce multiview vector-valued manifold regularization ((MVMR)-M-3) to integrate multiple features. (MVMR)-M-3 exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conduct extensive experiments on two challenging, but popular, datasets, PASCAL VOC' 07 and MIR Flickr, and validate the effectiveness of the proposed (MVMR)-M-3 for image classification.

• 出版日期2013-5
• 单位北京大学深圳研究生院; 北京大学