Mining from large image sets

作者: Luc Van Gool , Michael D. Breitenstein , Stephan Gammeter , Helmut Grabner , Till Quack

DOI: 10.1145/1646396.1646410

关键词:

摘要: So far, most image mining was based on interactive querying. Although such querying will remain important in the future, several applications need at wide scales that it has to run automatically. This adds an additional level problem, namely apply appropriate further processing different types of images, and decide automatically as well. paper touches those issues we discuss landmark images coming from webcams. The first part deals with automated collection landmarks, which are then also annotated enriched Wikipedia information. target application is users photograph landmarks their mobile phones or PDAs, get information about them. Similarly, can photo albums object interest be delineated images. pipeline propose actually retrieves more than manual keyword input would produce. second entirely source data, but one produces massive amounts (although typically not archived): They produce a single location, rather continuously over extended periods time. We approach summarize data handling quite applied

参考文章(29)
Jian Li, Shaogang Gong, Tao Xiang, Scene Segmentation for Behaviour Correlation european conference on computer vision. pp. 383- 395 ,(2008) , 10.1007/978-3-540-88693-8_28
Kevin Beyer, Jonathan Goldstein, Raghu Ramakrishnan, Uri Shaft, When Is ''Nearest Neighbor'' Meaningful? international conference on database theory. pp. 217- 235 ,(1999) , 10.1007/3-540-49257-7_15
Michal Perd'och, Ondrej Chum, Jiri Matas, Efficient representation of local geometry for large scale object retrieval computer vision and pattern recognition. pp. 9- 16 ,(2009) , 10.1109/CVPR.2009.5206529
Till Quack, Bastian Leibe, Luc Van Gool, World-scale mining of objects and events from community photo collections Proceedings of the 2008 international conference on Content-based image and video retrieval - CIVR '08. pp. 47- 56 ,(2008) , 10.1145/1386352.1386363
Neil Johnson, David Hogg, Learning the distribution of object trajectories for event recognition british machine vision conference. ,vol. 14, pp. 583- 592 ,(1995) , 10.1016/0262-8856(96)01101-8
Eric Grimson, Xiaogang Wang, Gee-Wah Ng, Keng Teck Ma, None, Trajectory analysis and semantic region modeling using a nonparametric Bayesian model computer vision and pattern recognition. pp. 1- 8 ,(2008) , 10.1109/CVPR.2008.4587718
Michael D. Breitenstein, Helmut Grabner, Luc Van Gool, Hunting Nessie - Real-time abnormality detection from webcams international conference on computer vision. pp. 1243- 1250 ,(2009) , 10.1109/ICCVW.2009.5457468
Dusan Omercevic, Ondrej Drbohlav, Ales Leonardis, High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors international conference on computer vision. pp. 1- 8 ,(2007) , 10.1109/ICCV.2007.4408880
Jean-François Lalonde, Srinivasa G. Narasimhan, Alexei A. Efros, What Does the Sky Tell Us about the Camera european conference on computer vision. pp. 354- 367 ,(2008) , 10.1007/978-3-540-88693-8_26
Isaac David Guedalia, Mickey London, Michael Werman, An on-line agglomerative clustering method for nonstationary data Neural Computation. ,vol. 11, pp. 521- 540 ,(1999) , 10.1162/089976699300016755