Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
International Journal of Computer Vision
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Video event detection using motion relativity and visual relatedness
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval
Computer Vision and Image Understanding
Query expansion for hash-based image object retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Weighting informativeness of bag-of-visual-words by kernel optimization for video concept detection
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
Visual query expansion via incremental hypernetwork models of image and text
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
| Hi-index | 0.00 |
Bag-of-visual-words (BoW) has been popular for visual classification in recent years. In this paper, we propose a novel BoW expansion method to alleviate the effect of visual word correlation problem. We achieve this by diffusing the weights of visual words in BoW based on visual word relatedness, which is rigorously defined within a visual ontology. The proposed method is tested in video indexing experiment on TRECVID-2006 video retrieval benchmark, and an improvement of 7% over the traditional BoW is reported.