A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Visual pattern discovery using web images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Relevance filtering meets active learning: improving web-based concept detectors
Proceedings of the international conference on Multimedia information retrieval
Region-based automatic web image selection
Proceedings of the international conference on Multimedia information retrieval
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
A novel method for semantic video concept learning using web images
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Automatic concept-to-query mapping for web-based concept detector training
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Leveraging social media for scalable object detection
Pattern Recognition
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A novel region-based approach is proposed to model semantic concepts using web images. Web images are mined to obtain multiple visual patterns automatically that then are used to model a semantic concept. First, the salient region groups corresponding to the representative visual patterns of a concept are mined and selected as positive samples. Next, a representative visual pattern is built in each salient region group by using a BDA classifier. Finally all the visual patterns are aggregated to describe the concept by using a BDA ensemble approach. Because the proposed method models a semantic concept utilizing multiple visual patterns, it enhances the visual variability of a visual model when learning from diverse web images and improves the robustness of the visual model in handling segmentation-related uncertainties. Experiment results demonstrate our method performs well on generic images including not only "object" concepts, but also complex "scene" concepts.