SCIENCE@home

Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification

Yong Luo,D. Tao,Chang Xu,Chao Xu,Hong Liu,Yonggang Wen

Published 2013 in IEEE Transactions on Neural Networks and Learning Systems

ABSTRACT

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 (MV3MR) to integrate multiple features. MV3MR 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 MV3MR for image classification.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-65 of 65 references · Page 1 of 1

CITED BY

Showing 1-100 of 153 citing papers · Page 1 of 2

12
Next