The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
Open accessAbstract
Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.
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Cites
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Cited by
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Self-citationRegion-based convolutional networks for accurate object detection and segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912
Edge Boxes: Locating Object Proposals from Edges
Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656
Unsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629
Self-citationUnsupervised Learning of Visual Representations Using Videos
Wang, X., & Gupta, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.320 · Cited by 1,005
Robust Higher Order Potentials for Enforcing Label Consistency
Kohli, P., Ladický, L., & Torr, P. H. S. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0202-0 · Cited by 853
Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification
Misra, I., Zitnick, C. L., & Hebert, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_32 · Cited by 779
Segmentation as selective search for object recognition
van de Sande, K. E. A., Uijlings, J. R. R., Gevers, T., & Smeulders, A. W. M. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126456 · Cited by 739
Class segmentation and object localization with superpixel neighborhoods
Fulkerson, B., Vedaldi, A., & Soatto, S. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459175 · Cited by 649
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
Carreira, J., & Sminchisescu, C. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.231 · Cited by 592
Discriminative clustering for image co-segmentation
Joulin, A., Bach, F., & Ponce, J. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539868 · Cited by 438
Deep multiple instance learning for image classification and auto-annotation
Wu, J., Yu, Y., Huang, C., & Yu, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298968 · Cited by 386
Factorizable Net: An Efficient Subgraph-Based Framework for Scene Graph Generation
Li, Y., Ouyang, W., Zhou, B., Shi, J., Zhang, C., & Wang, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01246-5_21 · Cited by 283
Joulin, A., Bach, F., & Ponce, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247719 · Cited by 279
Distributed cosegmentation via submodular optimization on anisotropic diffusion
Kim, G., Xing, E. P., Fei-Fei, L., & Kanade, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126239 · Cited by 270
Recovering Occlusion Boundaries from a Single Image
Hoiem, D., Stein, A. N., Efros, A. A., & Hebert, M. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408985 · Cited by 206
Self-citationGrauman, K., & Leibe, B. (2011) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00332ed1v01y201103aim011 · Cited by 126
Metadata
- Authors
- Russell, B. C., Freeman, W. T., Efros, A. A., Sivic, J., & Zisserman, A.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Pages
- 1605-1614
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2112301665
- Cited by
- 617
- References
- 3
- FWCI
- 35.06
- Type
- conference paper
- Language
- en
- Open access
- green
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