Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
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In real-world image understanding and retrieval applications, there exists a large number of images containing "verb-object" semantic. The most existing image annotation approaches which mainly focus on annotating images with "object" concepts may not well describe the image semantics. In this paper, we propose a novel image annotation approach by learning "verb-object" concepts. The "verb-object" concept learning method is developed based on the assumption that the classifiers of the "verb-object" concepts which contain the same object usually share a common structure. We formulate each "verb-object" concept classifier as a combination of a private part and a common part shared by all the "verb-object" concepts containing the same object. These classifiers are learned simultaneously through a joint optimization process. Experiments on a Web image data set containing 22,812 images with 28 concepts demonstrate that the proposed approach achieved promising performance compared to the baseline method.