Damian Borth
ABSTRACT
While online platforms like YouTube and Flickr do provide massive content for training of visual concept detectors, it remains a difficult challenge to retrieve the right training content from such platforms. In this technical demonstration we present lookapp, a system for the interactive construction of web-based concept detectors. It major features are an interactive "concept-to-query" mapping for training data acquisition and an efficient detector construction based on third party cloud computing services.
- Z. Chen, J. Cao, T. Xia, Y. Song, Y. Zhang, and J. Li. Web Video Retagging. Multimedia Tools and Applications, pages 1--30, 2010. Google Scholar
Digital Library
- J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In Int. Conf. Computer Vision and Pattern Recognition, 2009.Google Scholar
- J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih. Image Indexing Using Color Correlograms. In Int. Conf. on Pattern Recognition, 1997. Google ScholarDigital Library
- X. Li, C. Snoek, and M. Worring. Annotating Images by Harnessing Worldwide User-Tagged Photos. In Int. Conf. on Acoustics, Speech, and Signal Proc., 2009. Google ScholarDigital Library
- D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis., 60(2):91--110, 2004. Google ScholarDigital Library
- F. Seinstra, J. Geusebroek, D. Koelma, C. Snoek, M. Worring, and A. Smeulders. High-Performance Distributed Video Content Analysis with Parallel-Horus. IEEE Multimedia, 14(4):64--75, 2007. Google ScholarDigital Library
- J. Sivic and A. Zisserman. Video Google: A Text Retrieval Approach to Object Matching in Videos. In Int. Conf. Computer Vision, pages 1470--1477, 2003. Google ScholarDigital Library
- A. Ulges. Visual Concept Learning from User-tagged Web Video. Phd-thesis, Univ. of Kaiserslautern, 2009.Google Scholar
- A. Ulges, C. Schulze, M. Koch, and T. Breuel. Learning Automatic Concept Detectors from Online Video. Computer Vision Image Understanding, 2009. Google ScholarDigital Library
- R. Yan, M. O. Fleury, M. Merler, A. Natsev, and J. R. Smith. Large-Scale Multimedia Semantic Concept Modeling using Robust Subspace Bagging and Mapreduce. In Proc. Workshop on Large-scale Multimedia Retrieval and Mining, pages 35--42, 2009. Google ScholarDigital Library
Index Terms
- Lookapp: interactive construction of web-based concept detectors
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