---
OA_type: closed access
_id: '3700'
abstract:
- lang: eng
  text: We propose a new method to partition an unlabeled dataset, called Discriminative
    Context Partitioning (DCP). It is motivated by the idea of splitting the dataset
    based only on how well the resulting parts can be separated from a context class
    of disjoint data points. This is in contrast to typical clustering techniques
    like K-means that are based on a generative model by implicitly or explicitly
    searching for modes in the distribution of samples. The discriminative criterion
    in DCP avoids the problems that density based methods have when the a priori assumption
    of multimodality is violated, when the number of samples becomes small in relation
    to the dimensionality of the feature space, or if the cluster sizes are strongly
    unbalanced. We formulate DCP‘s separation
    property as a large-margin criterion, and show how the resulting optimization
    problem can be solved efficiently. Experiments on the MNIST and USPS datasets
    of handwritten digits and on a subset of the Caltech256 dataset show that, given
    a suitable context, DCP can achieve good results even in situation where density-based
    clustering techniques fail.
acknowledgement: This work was funded in part by the EC project CLASS, IST 027978.
article_processing_charge: No
author:
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Lampert C. Partitioning of image datasets using discriminative context information.
    In: <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>. IEEE;
    2008:1-8. doi:<a href="https://doi.org/10.1109/CVPR.2008.4587448">10.1109/CVPR.2008.4587448</a>'
  apa: 'Lampert, C. (2008). Partitioning of image datasets using discriminative context
    information. In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>
    (pp. 1–8). Anchorage, AK, United States: IEEE. <a href="https://doi.org/10.1109/CVPR.2008.4587448">https://doi.org/10.1109/CVPR.2008.4587448</a>'
  chicago: Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative
    Context Information.” In <i>2008 IEEE Conference on Computer Vision and Pattern
    Recognition</i>, 1–8. IEEE, 2008. <a href="https://doi.org/10.1109/CVPR.2008.4587448">https://doi.org/10.1109/CVPR.2008.4587448</a>.
  ieee: C. Lampert, “Partitioning of image datasets using discriminative context information,”
    in <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, Anchorage,
    AK, United States, 2008, pp. 1–8.
  ista: 'Lampert C. 2008. Partitioning of image datasets using discriminative context
    information. 2008 IEEE Conference on Computer Vision and Pattern Recognition.
    CVPR: Computer Vision and Pattern Recognition, 1–8.'
  mla: Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative Context
    Information.” <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>,
    IEEE, 2008, pp. 1–8, doi:<a href="https://doi.org/10.1109/CVPR.2008.4587448">10.1109/CVPR.2008.4587448</a>.
  short: C. Lampert, in:, 2008 IEEE Conference on Computer Vision and Pattern Recognition,
    IEEE, 2008, pp. 1–8.
conference:
  end_date: 2008-06-28
  location: Anchorage, AK, United States
  name: 'CVPR: Computer Vision and Pattern Recognition'
  start_date: 2008-06-23
date_created: 2018-12-11T12:04:41Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2026-06-12T11:40:17Z
day: '18'
doi: 10.1109/CVPR.2008.4587448
extern: '1'
language:
- iso: eng
main_file_link:
- url: http://pub.ist.ac.at/~chl/papers/lampert-cvpr2008b.pdf
month: '09'
oa_version: None
page: 1 - 8
publication: 2008 IEEE Conference on Computer Vision and Pattern Recognition
publication_identifier:
  isbn:
  - '9781424422425'
  issn:
  - 1063-6919
publication_status: published
publisher: IEEE
publist_id: '2657'
status: public
title: Partitioning of image datasets using discriminative context information
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2008'
...
---
_id: '3712'
abstract:
- lang: eng
  text: We present a new method for spectral clustering with paired data based on
    kernel canonical correlation analysis, called correlational spectral clustering.
    Paired data are common in real world data sources, such as images with text captions.
    Traditional spectral clustering algorithms either assume that data can be represented
    by a single similarity measure, or by co-occurrence matrices that are then used
    in biclustering. In contrast, the proposed method uses separate similarity measures
    for each data representation, and allows for projection of previously unseen data
    that are only observed in one representation (e.g. images but not text). We show
    that this algorithm generalizes traditional spectral clustering algorithms and
    show consistent empirical improvement over spectral clustering on a variety of
    datasets of images with associated text.
article_processing_charge: No
author:
- first_name: Matthew
  full_name: Blaschko, Matthew
  last_name: Blaschko
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Blaschko M, Lampert C. Correlational spectral clustering. In: <i>2008 IEEE
    Conference on Computer Vision and Pattern Recognition</i>. IEEE; 2008:1-8. doi:<a
    href="https://doi.org/10.1109/CVPR.2008.4587353">10.1109/CVPR.2008.4587353</a>'
  apa: 'Blaschko, M., &#38; Lampert, C. (2008). Correlational spectral clustering.
    In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i> (pp.
    1–8). Anchorage, AK, United States: IEEE. <a href="https://doi.org/10.1109/CVPR.2008.4587353">https://doi.org/10.1109/CVPR.2008.4587353</a>'
  chicago: Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering.”
    In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, 1–8.
    IEEE, 2008. <a href="https://doi.org/10.1109/CVPR.2008.4587353">https://doi.org/10.1109/CVPR.2008.4587353</a>.
  ieee: M. Blaschko and C. Lampert, “Correlational spectral clustering,” in <i>2008
    IEEE Conference on Computer Vision and Pattern Recognition</i>, Anchorage, AK,
    United States, 2008, pp. 1–8.
  ista: 'Blaschko M, Lampert C. 2008. Correlational spectral clustering. 2008 IEEE
    Conference on Computer Vision and Pattern Recognition. CVPR: Computer Vision and
    Pattern Recognition, 1–8.'
  mla: Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering.”
    <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, IEEE,
    2008, pp. 1–8, doi:<a href="https://doi.org/10.1109/CVPR.2008.4587353">10.1109/CVPR.2008.4587353</a>.
  short: M. Blaschko, C. Lampert, in:, 2008 IEEE Conference on Computer Vision and
    Pattern Recognition, IEEE, 2008, pp. 1–8.
conference:
  end_date: 2008-06-28
  location: Anchorage, AK, United States
  name: 'CVPR: Computer Vision and Pattern Recognition'
  start_date: 2008-06-23
date_created: 2018-12-11T12:04:45Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2026-06-12T11:32:08Z
day: '18'
doi: 10.1109/CVPR.2008.4587353
extern: '1'
language:
- iso: eng
month: '09'
oa_version: None
page: 1 - 8
publication: 2008 IEEE Conference on Computer Vision and Pattern Recognition
publication_identifier:
  isbn:
  - '9781424422425'
  issn:
  - 1063-6919
publication_status: published
publisher: IEEE
publist_id: '2646'
status: public
title: Correlational spectral clustering
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2008'
...
---
OA_type: closed access
_id: '3714'
abstract:
- lang: eng
  text: Most successful object recognition systems rely on binary classification,
    deciding only if an object is present or not, but not providing information on
    the actual object location. To perform localization, one can take a sliding window
    approach, but this strongly increases the computational cost, because the classifier
    function has to be evaluated over a large set of candidate subwindows. In this
    paper, we propose a simple yet powerful branchand- bound scheme that allows efficient
    maximization of a large class of classifier functions over all possible subimages.
    It converges to a globally optimal solution typically in sublinear time. We show
    how our method is applicable to different object detection and retrieval scenarios.
    The achieved speedup allows the use of classifiers for localization that formerly
    were considered too slow for this task, such as SVMs with a spatial pyramid kernel
    or nearest neighbor classifiers based on the 2-distance. We demonstrate state-of-the-art
    performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC
    2006 dataset and in the PASCAL VOC 2007 competition.
article_processing_charge: No
author:
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Matthew
  full_name: Blaschko, Matthew
  last_name: Blaschko
- first_name: Thomas
  full_name: Hofmann, Thomas
  last_name: Hofmann
citation:
  ama: 'Lampert C, Blaschko M, Hofmann T. Beyond sliding windows: Object localization
    by efficient subwindow search. In: <i>2008 IEEE Conference on Computer Vision
    and Pattern Recognition</i>. IEEE; 2008:1-8. doi:<a href="https://doi.org/10.1109/CVPR.2008.4587586">10.1109/CVPR.2008.4587586</a>'
  apa: 'Lampert, C., Blaschko, M., &#38; Hofmann, T. (2008). Beyond sliding windows:
    Object localization by efficient subwindow search. In <i>2008 IEEE Conference
    on Computer Vision and Pattern Recognition</i> (pp. 1–8). Anchorage, AK, United
    States: IEEE. <a href="https://doi.org/10.1109/CVPR.2008.4587586">https://doi.org/10.1109/CVPR.2008.4587586</a>'
  chicago: 'Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Beyond Sliding
    Windows: Object Localization by Efficient Subwindow Search.” In <i>2008 IEEE Conference
    on Computer Vision and Pattern Recognition</i>, 1–8. IEEE, 2008. <a href="https://doi.org/10.1109/CVPR.2008.4587586">https://doi.org/10.1109/CVPR.2008.4587586</a>.'
  ieee: 'C. Lampert, M. Blaschko, and T. Hofmann, “Beyond sliding windows: Object
    localization by efficient subwindow search,” in <i>2008 IEEE Conference on Computer
    Vision and Pattern Recognition</i>, Anchorage, AK, United States, 2008, pp. 1–8.'
  ista: 'Lampert C, Blaschko M, Hofmann T. 2008. Beyond sliding windows: Object localization
    by efficient subwindow search. 2008 IEEE Conference on Computer Vision and Pattern
    Recognition. CVPR: Computer Vision and Pattern Recognition, 1–8.'
  mla: 'Lampert, Christoph, et al. “Beyond Sliding Windows: Object Localization by
    Efficient Subwindow Search.” <i>2008 IEEE Conference on Computer Vision and Pattern
    Recognition</i>, IEEE, 2008, pp. 1–8, doi:<a href="https://doi.org/10.1109/CVPR.2008.4587586">10.1109/CVPR.2008.4587586</a>.'
  short: C. Lampert, M. Blaschko, T. Hofmann, in:, 2008 IEEE Conference on Computer
    Vision and Pattern Recognition, IEEE, 2008, pp. 1–8.
conference:
  end_date: 2008-06-28
  location: Anchorage, AK, United States
  name: 'CVPR: Computer Vision and Pattern Recognition'
  start_date: 2008-06-23
date_created: 2018-12-11T12:04:46Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2026-06-12T11:29:05Z
day: '18'
doi: 10.1109/CVPR.2008.4587586
extern: '1'
language:
- iso: eng
main_file_link:
- url: http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf5070.pdf
month: '09'
oa_version: None
page: 1 - 8
publication: 2008 IEEE Conference on Computer Vision and Pattern Recognition
publication_identifier:
  isbn:
  - '9781424422425'
  issn:
  - 1063-6919
publication_status: published
publisher: IEEE
publist_id: '2644'
status: public
title: 'Beyond sliding windows: Object localization by efficient subwindow search'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2008'
...