Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification

• Published in: IEEE transaction on neural networks and learning systems Vol. 24; no. 5; pp. 709 - 722
• Main Authors: Luo, Yong, Tao, Dacheng, Xu, Chang, Xu, Chao, Liu, Hong, Wen, Yonggang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Correlation; Data processing. List processing. Character string processing; Database Management Systems; Exact sciences and technology; Image classification; Image color analysis; Image Interpretation, Computer-Assisted; Kernel; Labels; Laplace equations; manifold; Manifolds; Memory organisation. Data processing; Models, Statistical; multilabel; multiview; Neural networks; Neural Networks (Computer); Optimization; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Regularization; Reproducibility of Results; semisupervised; Software; Support vector machines; Surface layer; Texture; multiview; Computer vision; Tree(graph); Image processing; Multi-label classification; Pedestrian; semisupervised; Bicycle; Texture; Color image; Multiple image; Multiple view; Walking; multilabel; Manifold; Efficiency; Semi-supervised learning; Regularization; Image classification

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, textu...