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2010 international conference on pattern recognition unsupervised ensemble ranking: application to large-scale image retrieval.
| Content Provider | CiteSeerX |
|---|---|
| Author | Lee, Jung-Eun Jin, Rong Jain, Anil K. |
| Abstract | Abstract—The continued explosion in the growth of image and video databases makes automatic image search and retrieval an extremely important problem. Among the various approaches to Content-based Image Retrieval (CBIR), image similarity based on local point descriptors has shown promising performance. However, this approach suffers from the scalability problem. Although bag-of-words model resolves the scalability problem, it suffers from loss in retrieval accuracy. We circumvent this performance loss by an ensemble ranking approach in which rankings from multiple bag-of-words models are combined to obtain more accurate retrieval results. An unsupervised algorithm is developed to learn the weights for fusing the rankings from multiple bag-of-words models. Experimental results on a database of 100, 000 images show that this approach is both efficient and effective in finding visually similar images. Keywords-Near-duplicate image retrieval, Bag-of-words models, Tattoo images, Ensemble ranking I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Ensemble Ranking Large-scale Image Retrieval Pattern Recognition International Conference Scalability Problem Multiple Bag-of-words Model Bag-of-words Model Local Point Descriptor Similar Image Promising Performance Important Problem Ensemble Ranking Approach Content-based Image Retrieval Various Approach Unsupervised Algorithm Keywords-near-duplicate Image Retrieval Continued Explosion Accurate Retrieval Result Performance Loss Image Similarity Automatic Image Search Retrieval Accuracy Video Database Experimental Result |
| Content Type | Text |
