Skip to main content
SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
  1. Home
  2. International Journal of Computer Vision
  3. Article

Gaussian Processes for Object Categorization

  • Open Access
  • Published: 16 July 2009
  • volume 88, pages 169–188 (2010)
Download PDF

You have full access to this open access article

International Journal of Computer Vision Aims and scope Submit manuscript
Gaussian Processes for Object Categorization
Download PDF
  • Ashish Kapoor1,
  • Kristen Grauman2,
  • Raquel Urtasun3 &
  • …
  • Trevor Darrell3 
  • 2369 Accesses

  • 120 Citations

  • Explore all metrics

  • Cite this article

Abstract

Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gaussian Processes (GPs) provide a framework for deriving regression techniques with explicit uncertainty models; we show here how Gaussian Processes with covariance functions defined based on a Pyramid Match Kernel (PMK) can be used for probabilistic object category recognition. Our probabilistic formulation provides a principled way to learn hyperparameters, which we utilize to learn an optimal combination of multiple covariance functions. It also offers confidence estimates at test points, and naturally allows for an active learning paradigm in which points are optimally selected for interactive labeling. We show that with an appropriate combination of kernels a significant boost in classification performance is possible. Further, our experiments indicate the utility of active learning with probabilistic predictive models, especially when the amount of training data labels that may be sought for a category is ultimately very small.

Download to read the full article text

Use our pre-submission checklist

Avoid common mistakes on your manuscript.

References

  • Abramson, Y., & Freund, Y. (2004). Active learning for visual object recognition (Technical report). UCSD.

  • Belongie, S., Malik, J., & Puzicha, J. (2001). Matching shapes. In ICCV.

  • Berg, A., & Malik, J. (2001). Geometric blur for template matching. In CVPR.

  • Boiman, O., Shechtman, E., & Irani, M. (2008). In defense of nearest-neighbor based image classification. In CVPR.

  • Bosch, A., Zisserman, A., & Muñoz, X. (2007). Representing shape with a spatial pyramid kernel. In CIVR.

  • Chang, C., & Lin, C. (2001). LIBSVM: a library for SVMs.

  • Chang, E. Y., Tong, S., Goh, K., & Chang, C. (2005). Support vector machine concept-dependent active learning for image retrieval. IEEE Transactions on Multimedia.

  • Chum, O., & Zisserman, A. (2007). An exemplar model for learning object classes. In Proceedings of the IEEE conference on computer vision and pattern recognition.

  • Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13(1).

  • Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transaction on Pattern Recognition and Machine Intelligence.

  • Fergus, R., Perona, P., & Zisserman, A. (2003). Object class recognition by unsupervised scale-invariant learning. In CVPR.

  • Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine Learning, 28(2–3).

  • Frome, A., Singer, Y., Sha, F., & Malik, J. (2007). Learning globally-consistent local distance functions for shape-based image retrieval and classification. In ICCV.

  • Grauman, K., & Darrell, T. (2005). The pyramid match kernel: Discriminative classification with sets of image features. In ICCV.

  • Grauman, K., & Darrell, T. (2006a). Approximate correspondences in high dimensions. In NIPS.

  • Grauman, K., & Darrell, T. (2006b). Unsupervised learning of categories from sets of partially matching image features. In CVPR.

  • Kadir, T., & Brady, M. (2003). Scale saliency: A novel approach to salient feature and scale selection. In International conference visual information engineering.

  • Kapoor, A., Grauman, K., Urtasun, R., & Darrell, T. (2007). Active learning with Gaussian processes for object categorization. In ICCV.

  • Kim, H. C., Kim, D., Ghahramani, Z., & Bang, S. Y. (2006). Appearance-based gender classification with Gaussian processes. Pattern Recognition Letters.

  • Krause, A., Singh, A., & Guestrin, C. (2008). Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. In JMLR.

  • Kumar, A., & Sminchisescu, C. (2007). Support kernel machines for object recognition. In ICCV.

  • Lawrence, N. (2004). Gaussian process latent variable models for visualisation of high dimensional data. In NIPS.

  • Lawrence, N., Seeger, M., & Herbrich, R. (2002). Fast sparse Gaussian process method: Informative vector machines. In NIPS.

  • Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR.

  • Lin, Y. Y., Liu, T. Y., & Fuh, C. S. (2007). Local ensemble kernel learning for object category recognition. In CVPR.

  • Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. IJCV, 60(2).

  • MacKay, D. (1992) Information-based objective functions for active data selection. Neural Computation, 4(4).

  • McCallum, A. K., & Nigam, K. (1998). Employing EM in pool-based active learning for text classification. In ICML.

  • Mikolajczyk, K., & Schmid, C. (2001). Indexing based on scale invariant interest points. In ICCV.

  • Mikolajczyk, K., & Schmid, C. (2004). Scale and affine invariant interest point detectors. IJCV, 1(60), 63–86.

    Article  Google Scholar 

  • Minka, T. P. (2001). A family of algorithms for approximate Bayesian inference. PhD thesis, MIT.

  • Moosmann, B. T. F., & Jurie, F. (2007). Fast discriminative visual codebooks using randomized clustering forests. In NIPS.

  • Muslea, I., Minton, S., & Knoblock, C. A. (2002). Active + semi-supervised learning = robust multi-view learning. In ICML.

  • Nister, D., & Stewenius, H. (2006). Scalable recognition with a vocabulary tree. In CVPR.

  • Nowak, E., Jurie, F., & Triggs, B. (2006). Sampling strategies for bag-of-features image classification. In ECCV.

  • Rasmusen, C. E., & Williams, C. (2006). Gaussian processes for machine learning. Cambridge: MIT Press.

    Google Scholar 

  • Seeger, M. (2004). Gaussian processes for machine learning. International Journal of Neural Systems, 14(2).

  • Shen, Y., Ng, A., & Seeger, M. (2006). Fast Gaussian process regression using kd-trees. In NIPS.

  • Sivic, J., & Zisserman, A. (2003). Video Google: a text retrieval approach to object matching in videos. In ICCV.

  • Sivic, J., Russell, B., Efros, A., Zisserman, A., & Freeman, W. (2005). Discovering object categories in image collections. In ICCV.

  • Snelson, E., & Ghahramani, Z. (2006). Sparse Gaussian processes using pseudo-inputs. In NIPS.

  • Sudderth, E., Torralba, A., Freeman, W., & Willsky, A. (2005). Describing visual scenes using transformed Dirichlet processes. In NIPS.

  • Tong, S., & Koller, D. (2000). Support vector machine active learning with applications to text classification. In ICML.

  • Tresp, V. (2000). Mixtures of Gaussian processes. In NIPS.

  • Tsang, I. W.-H., & Kwok, J. T.-Y. (2006). Efficient hyperkernel learning using second-order cone programming. IEEE Transactions on Neural Networks.

  • Urtasun, R., & Darrell, T. (2008). Local probabilistic regression for activity-independent human pose inference. In CVPR.

  • Urtasun, R., Fleet, D. J., Hertzman, A., & Fua, P. (2005). Priors for people tracking from small training sets. In ICCV.

  • Urtasun, R., Fleet, D. J., & Fua, P. (2006). Gaussian process dynamical models for 3d people tracking. In CVPR.

  • Varma, M., & Ray, D. (2007). Learning the discriminative power-invariance trade-off. In ICCV.

  • von Ahn, L., & Dabbish, L. (2004). Labeling images with a computer game. In ACM CHI.

  • von Ahn, L., Liu, R., & Blum, M. (2006). Peekaboom: A game for locating objects in images. In ACM CHI.

  • Wallraven, C., Caputo, B., & Graf, A. (2003). Recognition with local features: the kernel recipe. In ICCV.

  • Williams, C., & Barber, D. (1998). Bayesian classification with Gaussian processes. IEEE Transaction on Pattern Recognition and Machine Intelligence, 20(12), 1342–1351.

    Article  Google Scholar 

  • Williams, O. (2006). A switched Gaussian process for estimating disparity and segmentation in binocular stereo. In NIPS.

  • Zhang, H., Berg, A., Maire, M., & Malik, J. (2006). SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In CVPR.

  • Zhu, X., Lafferty, J., & Ghahramani, Z. (2003). Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. In Workshop on the continuum from labeled to unlabeled data in machine learning and data mining at ICML.

Download references

Author information

Authors and Affiliations

  1. Microsoft Research, Redmond, WA, 98052, USA

    Ashish Kapoor

  2. University of Texas at Austin, Austin, TX, 78712, USA

    Kristen Grauman

  3. UC Berkeley EECS & ICSI, Berkeley, CA, 94720, USA

    Raquel Urtasun & Trevor Darrell

Authors
  1. Ashish Kapoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kristen Grauman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raquel Urtasun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Trevor Darrell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish Kapoor.

Rights and permissions

Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Reprints and Permissions

About this article

Cite this article

Kapoor, A., Grauman, K., Urtasun, R. et al. Gaussian Processes for Object Categorization. Int J Comput Vis 88, 169–188 (2010). https://doi.org/10.1007/s11263-009-0268-3

Download citation

  • Received: 22 July 2008

  • Accepted: 01 July 2009

  • Published: 16 July 2009

  • Issue Date: June 2010

  • DOI: https://doi.org/10.1007/s11263-009-0268-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Object recognition
  • Gaussian process
  • Kernel combination
  • Active learning
Use our pre-submission checklist

Avoid common mistakes on your manuscript.

Advertisement

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

Not affiliated

Springer Nature

© 2023 Springer Nature