Machine Learning - Special issue on inductive transfer
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints
SIAM Journal on Numerical Analysis
Convex multi-task feature learning
Machine Learning
Proceedings of the 18th international conference on World wide web
Group lasso with overlap and graph lasso
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
Efficient large-scale image annotation by probabilistic collaborative multi-label propagation
Proceedings of the international conference on Multimedia
Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
SIAM Journal on Imaging Sciences
Content-Based affective image classification and retrieval using support vector machines
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Image query by impression words-the IQI system
IEEE Transactions on Consumer Electronics
Proceedings of the 20th ACM international conference on Multimedia
Annotating web images using NOVA: NOn-conVex group spArsity
Proceedings of the 20th ACM international conference on Multimedia
Robust image annotation via simultaneous feature and sample outlier pursuit
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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To bridge the semantic gap between low level feature and human perception, most of the existing algorithms aim mainly at annotating images with concepts coming from only one semantic space, e.g. cognitive or affective. The naive combination of the outputs from these spaces will implicitly force the conditional independence and ignore the correlations among the spaces. In this paper, to exploit the comprehensive semantic of images, we propose a general framework for harmoniously integrating the above multiple semantics, and investigating the problem of learning to annotate images with training images labeled in two or more correlated semantic spaces, such as fascinating nighttime, or exciting cat. This kind of semantic annotation is more oriented to real world search scenario. Our proposed approach outperforms the baseline algorithms by making the following contributions. 1) Unlike previous methods that annotate images within only one semantic space, our proposed multi-semantic annotation associates each image with labels from multiple semantic spaces. 2) We develop a multi-task linear discriminative model to learn a linear mapping from features to labels. The tasks are correlated by imposing the exclusive group lasso regularization for competitive feature selection, and the graph Laplacian regularization to deal with insufficient training sample issue. 3) A Nesterov-type smoothing approximation algorithm is presented for efficient optimization of our model. Extensive experiments on NUS-WIDEEmotive dataset (56k images) with 8×81 emotive cognitive concepts and Object&Scene datasets from NUS-WIDE well validate the effectiveness of the proposed approach.