  Image/video data is usually represented with multiple visual features. Fusion
of multi-source information for establishing the attributes has been widely
recognized. Multi-feature visual recognition has recently received much
attention in multimedia applications. This paper studies visual understanding
via a newly proposed l_2-norm based multi-feature shared learning framework,
which can simultaneously learn a global label matrix and multiple
sub-classifiers with the labeled multi-feature data. Additionally, a group
graph manifold regularizer composed of the Laplacian and Hessian graph is
proposed for better preserving the manifold structure of each feature, such
that the label prediction power is much improved through the semi-supervised
learning with global label consistency. For convenience, we call the proposed
approach Global-Label-Consistent Classifier (GLCC). The merits of the proposed
method include: 1) the manifold structure information of each feature is
exploited in learning, resulting in a more faithful classification owing to the
global label consistency; 2) a group graph manifold regularizer based on the
Laplacian and Hessian regularization is constructed; 3) an efficient
alternative optimization method is introduced as a fast solver owing to the
convex sub-problems. Experiments on several benchmark visual datasets for
multimedia understanding, such as the 17-category Oxford Flower dataset, the
challenging 101-category Caltech dataset, the YouTube & Consumer Videos dataset
and the large-scale NUS-WIDE dataset, demonstrate that the proposed approach
compares favorably with the state-of-the-art algorithms. An extensive
experiment on the deep convolutional activation features also show the
effectiveness of the proposed approach. The code is available on
http://www.escience.cn/people/lei/index.html
