Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Kodak's consumer video benchmark data set: concept definition and annotation
Proceedings of the international workshop on Workshop on multimedia information retrieval
Proceedings of the 25th international conference on Machine learning
Extracting shared subspace for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Linear dimensionality reduction for multi-label classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Heterogeneous feature selection by group lasso with logistic regression
Proceedings of the international conference on Multimedia
Annotating web images using NOVA: NOn-conVex group spArsity
Proceedings of the 20th ACM international conference on Multimedia
Image annotation by semi-supervised cross-domain learning with group sparsity
Journal of Visual Communication and Image Representation
Real web community based automatic image annotation
Computers and Electrical Engineering
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We can obtain more and more kinds of heterogeneous features (such as color, shape and texture) in images which can be extracted to describe various aspects of visual characteristics. Those high-dimensional heterogeneous visual features are intrinsically embedded in a non-linear space. In order to effectively utilize these heterogeneous features, this paper proposes an approach, called Composite Kernel Learning with Group Structure (CKLGS), to select groups of discriminative features for image annotation. For each image label, the CKLGS method embeds the nonlinear image data with discriminative features into different Reproducing Kernel Hilbert Spaces (RKHS), and then composes these kernels to select groups of discriminative features. Thus a classification model can be trained for image annotation. By the comparisons with other image annotation algorithms, experiments show that the proposed CKLGS for image annotation achieves a better performance.