The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Structured Learning and Prediction in Computer Vision
Abstract
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. This monograph introduces the reader to the most popular classes of structured models in computer vision. Our focus is discrete undirected graphical models which we cover in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference. We discuss separately recently successful techniques for prediction in general structured models. In the second part of this monograph we describe methods for parameter learning where we distinguish the classic maximum likelihood based methods from the more recent prediction-based parameter learning methods. We highlight developments to enhance current models and discuss kernelized models and latent variable models. To make the monograph more practical and to provide links to further study we provide examples of successful application of many methods in the computer vision literature.
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Cites
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486 · cited 5×
“Propagation and the Sum-Product Algorithm The sum-product algorithm [Kschischang et al., 2001; MacKay, 2003] is a dynamic programming algorithm that given a discrete distribution in the form (2.5) computes the normalizing constant Z and the marginal distributions p(y i ) and p(y F ) for all…”
BackgroundMethodInfluentialTraining Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“This is possible because of a crucial insight by Hinton [61]: instead of waiting for the Markov chain to converge against its equilibrium distribution before computing a gradient approximation, we can use samples from the very beginning of the Markov chain.”
MethodAn Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
The Representation and Matching of Pictorial Structures
Fischler, M. A., & Elschlager, R. A. (1973) · IEEE Transactions on Computers · doi:10.1109/t-c.1973.223602 · Cited by 1,322
Cited by
Human Pose Estimation with Iterative Error Feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.512 · Cited by 805
Smart “Predict, then Optimize”
Elmachtoub, A. N., & Grigas, P. (2022) · Management Science · doi:10.1287/mnsc.2020.3922 · Cited by 773
A deep learning approach for the analysis of masses in mammograms with minimal user intervention
Dhungel, N., Carneiro, G., & Bradley, A. P. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.01.009 · Cited by 336
Ghamisi, P., Maggiori, E., Li, S., Souza, R., Tarablaka, Y., Moser, G., De Giorgi, A., Fang, L., Chen, Y., Chi, M., et al. (2018) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2018.2854840 · Cited by 325
Semantic segmentation of urban scenes by learning local class interactions
Volpi, M., & Ferrari, V. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301377 · Cited by 152
Metadata
- Authors
- Nowozin, S., & Lampert, C. H.
- Venue
- Foundations and Trends in Computer Graphics and Vision
- Publisher
- Emerald
- Published
- 2011-05-23
- Volume
- 6
- Issue
- 3-4
- Pages
- 185-365
- ISSN
- 1572-2740 (ISSN-L)
- 1572-2759
- OpenAlex
- W4206733017
- Cited by
- 178
- References
- 6
- FWCI
- 10.05
- Type
- journal article
- Language
- en
- Open access
- closed
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