An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Kodak's consumer video benchmark data set: concept definition and annotation
Proceedings of the international workshop on Workshop on multimedia information retrieval
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
Image annotation by composite kernel learning with group structure
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Exploiting the entire feature space with sparsity for automatic image annotation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
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
Editor's Choice Article: Sparse feature selection based on graph Laplacian for web image annotation
Image and Vision Computing
Embedded local feature selection within mixture of experts
Information Sciences: an International Journal
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The selection of groups of discriminative features is critical for image understanding since the irrelevant features could deteriorate the performance of image understanding. This paper formulates the selection of groups of discriminative features by the extension of group lasso with logistic regression for high-dimensional feature setting, we call it as the heterogeneous feature selection by Group Lasso with Logistic Regression (GLLR). GLLR encodes a sparse grouping prior to seek after a more interpretable model for feature selection and can identify most of discriminative groups of homogeneous features. The utilization of GLLR for image annotation shows the proposed GLLR achieves a better performance.