Agradecimientos:
This work has been partially supported by the project AFICUS, co-funded by the Spanish Ministry of Industry, Trade and Tourism, and the European Fund for Regional Development, with Ref.: TSI-020110- 2009-103, and the National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.
This paper proposes a probabilistic generative model that concurrently tackles the problems of image retrieval and detection of the region-of-interest (ROI). By introducing a latent variable that classifies the matches as true or false, we specifically focus oThis paper proposes a probabilistic generative model that concurrently tackles the problems of image retrieval and detection of the region-of-interest (ROI). By introducing a latent variable that classifies the matches as true or false, we specifically focus on the application of geometric constrains to the keypoint matching process and the achievement of robust estimates of the geometric transformation between two images showing the same object. Our experiments in a challenging image retrieval database demonstrate that our approach outperforms the most prevalent approach for geometrically constrained matching, and compares favorably to other state-of-the-art methods. Furthermore, the proposed technique concurrently provides very good segmentations of the region of interest.[+][-]
Nota:
Proceedings of: 10th International Workshop on Content-Based Multimedia Indexing (CBMI). Annecy, France, 27-29 June 2012.
