Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
To construct optimal training set for video annotation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
A novel region-based approach to visual concept modeling using web images
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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In recent years, exploring the rich web image resources has been offering promising solutions to the problem of how to perform low-manual-cost concept learning. However, concept classifiers trained using web images perform poorly when they are directly applied to video concept detection. We propose a novel scheme to address video concept learning using web images, one that includes the selection of web training data and the transfer of subspace learning within a unified framework. Starting with a small set of video keyframes related to a video concept, we select web training data of good quality from the web by referring to the content of video keyframes. Then, by exploiting both the selected dataset and video keyframes, we train a robust concept classifier by means of a transfer subspace learning method. Experiment results demonstrate the robustness and effectiveness of our method.