Content-Based Image Retrieval Using Multiple-Instance Learning
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Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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Learning Multiple Tasks with Kernel Methods
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Large Margin Methods for Structured and Interdependent Output Variables
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MILES: Multiple-Instance Learning via Embedded Instance Selection
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An adaptive graph model for automatic image annotation
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Working Set Selection Using Second Order Information for Training Support Vector Machines
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Correlative multi-label video annotation
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Cutting-plane training of structural SVMs
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Correlative linear neighborhood propagation for video annotation
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Automatic image tagging through information propagation in a query log based graph structure
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Personalizing automated image annotation using cross-entropy
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Tag suggestion and localization for web videos by bipartite graph matching
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Multimedia information retrieval on the social web
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Using tagged images of low visual ambiguity to boost the learning efficiency of object detectors
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Large-scale loosely-tagged images (i.e., multiple object tags are given loosely at the image level) are available on Internet, and it is very attractive to leverage such loosely-tagged images for automatic image annotation applications. In this paper, a multi-task structured SVM algorithm is developed to leverage both the inter-object correlations and the loosely-tagged images for achieving more effective training of a large number of inter-related object classifiers. To leverage the loosely-tagged images for object classifier training, each loosely-tagged image is partitioned into a set of image instances (image regions) and a multiple instance learning algorithm is developed for instance label identification by automatically identifying the correspondences between multiple tags (given at the image level) and the image instances. An object correlation network is constructed for characterizing the inter-object correlations explicitly and identifying the inter-related learning tasks automatically. To enhance the discrimination power of a large number of inter-related object classifiers, a multi-task structured SVM algorithm is developed to model the inter-task relatedness more precisely and leverage the inter-object correlations for classifier training. Our experiments on a large number of inter-related object classes have provided very positive results.