Article

Selecting what is important: training visual attention

Authors:

Simone Frintrop,

Gerriet Backer,

Erich RomeAuthors Info & Claims

KI'05: Proceedings of the 28th annual German conference on Advances in Artificial Intelligence

Pages 351 - 365

https://doi.org/10.1007/11551263_28

Published: 11 September 2005 Publication History

Abstract

We present a new, sophisticated algorithm to select suitable training images for our biologically motivated attention system VOCUS. The system detects regions of interest depending on bottom-up (scene-dependent) and top-down (target-specific) cues. The top-down cues are learned by VOCUS from one or several training images. We show that our algorithm chooses a subset of the training set that outperforms both the selection of one single image as well as simply using all available images for learning. With this algorithm, VOCUS is able to quickly and robustly detect targets in numerous real-world scenes.

References

[1]

Backer, G., Mertsching, B. and Bollmann, M. Data- and model-driven Gaze Control for an Active-Vision System. IEEE Trans. on PAMI, 23(12) (2001) 1415-1429.

Digital Library

[2]

Corbetta, M. and Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews, 3 (3, 2002) 201-215.

[3]

Frintrop, S. VOCUS: A Visual Attention System for Object Detection and Goal-directed Search. PhD thesis University of Bonn Germany (to appear 2005).

[4]

Frintrop, S., Backer, G. and Rome, E. Goal-directed Search with a Top-down Modulated Computational Attention System. In: Proc. of DAGM 2005 (accepted) Lecture Notes in Computer Science (LNCS) Springer (2005).

Digital Library

[5]

Hamker, F. Modeling Attention: From computational neuroscience to computer vision. In: Proc. of WAPCV'04 (2004) 59-66.

Digital Library

[6]

Hunt, R. W. G. Measuring colour, Ellis Horwood Limited Chichester, West Sussex, England 1991.

[7]

Itti, L., Koch, C. and Niebur, E. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans. on PAMI, 20 (11, 1998) 1254-1259.

Digital Library

[8]

Koch, C. and Ullman, S. Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4 (4, 1985) 219-227.

[9]

Mitri, S., Frintrop, S., Pervölz, K., Surmann, H. and Nüchter, A. Robust Object Detection at Regions of Interest with an Application in Ball Recognition. In: Proc. of the Int'l Conf. on Robotics and Automation (ICRA '05) (to appear 2005).

[10]

Navalpakkam, V., Rebesco, J. and Itti, L. Modeling the influence of task on attention. Vision Research, 45 (2, 2005) 205-231.

[11]

Neisser, U. Cognitive Psychology, Appleton-Century-Crofts New York 1967.

[12]

Palmer, S. E. Vision Science, Photons to Phenomenology, The MIT Press 1999.

[13]

Schill, K., Umkehrer, E., Beinlich, S., Krieger, G. and Zetzsche, C. Scene analysis with saccadic eye movements: Top-down and bottom-up modeling. Journal of Electronic Imaging, 10 (1, 2001) 152-160.

[14]

Sun, Y. and Fisher, R. Object-based visual attention for computer vision. Artificial Intelligence, 146 (1, 2003) 77-123.

Digital Library

[15]

Theeuwes, J. Top-down search strategies cannot override attentional capture. Psychonomic Bulletin & Review, 11 (2004) 65-70.

[16]

Treisman, A. M. and Gelade, G. A feature integration theory of attention. Cognitive Psychology, 12 (1980) 97-136.

[17]

Tsotsos, J. K. Complexity, Vision, and Attention. In: Vision and Attention, M. Jenkin and L. R. Harris (Eds.) Springer Verlag 2001 chapter 6.

[18]

Tsotsos, J. K., Culhane, S. M., Wai, W. Y. K., Lai, Y., Davis, N. and Nuflo, F. Modeling Visual Attention via Selective Tuning. AI, 78 (1-2, 1995) 507-545.

Digital Library

[19]

Wolfe, J. Guided Search 2.0: A Revised Model of Visual Search. Psychonomic Bulletin & Review, 1 (2, 1994) 202-238.

Cited By

Krause ECantrell RPotapova EZillich MScheutz MGini MShehory OIto TJonker C(2013)Incrementally biasing visual search using natural language inputProceedings of the 2013 international conference on Autonomous agents and multi-agent systems10.5555/2484920.2484929(31-38)Online publication date: 6-May-2013
https://dl.acm.org/doi/10.5555/2484920.2484929
Frintrop SBacker GRome E(2005)Goal-directed search with a top-down modulated computational attention systemProceedings of the 27th DAGM conference on Pattern Recognition10.1007/11550518_15(117-124)Online publication date: 31-Aug-2005
https://dl.acm.org/doi/10.1007/11550518_15

Selecting what is important: training visual attention
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

Recommendations

A pre-selecting base kernel method in multiple kernel learning

The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels ...
Selecting Suitable Image Retargeting Methods with Multi-instance Multi-label Learning
BHI 2013: Proceedings of the International Conference on Brain and Health Informatics - Volume 8211

Althogh the diversity of mobile devices brings in image retargeting technique to effectively display images on various screens, no existing image retargeting method can handle all images well. In this paper, we propose a novel approach to select ...
Safely selecting subsets of training data
DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

Highly versatile classifiers for document analysis systems demand representative training sets which can be dauntingly large, often challenging conventional trainable classifier technologies. We propose to select a small subset of training data, matched ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

KI'05: Proceedings of the 28th annual German conference on Advances in Artificial Intelligence

September 2005

409 pages

ISBN:3540287612

Editor:
Ulrich Furbach
Department of Computer Science, Artificial Intelligence Research Group, University of Koblenz-Landau,, Universitätsstr. 1, Koblenz

Sponsors

Griesson - de Beukelaer GmbH: Griesson - de Beukelaer GmbH
City of Koblenz: City of Koblenz
Government of Rhineland-Palatine: Government of Rhineland-Palatine
University of Koblenz-Landau

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 September 2005

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Krause ECantrell RPotapova EZillich MScheutz MGini MShehory OIto TJonker C(2013)Incrementally biasing visual search using natural language inputProceedings of the 2013 international conference on Autonomous agents and multi-agent systems10.5555/2484920.2484929(31-38)Online publication date: 6-May-2013
https://dl.acm.org/doi/10.5555/2484920.2484929
Frintrop SBacker GRome E(2005)Goal-directed search with a top-down modulated computational attention systemProceedings of the 27th DAGM conference on Pattern Recognition10.1007/11550518_15(117-124)Online publication date: 31-Aug-2005
https://dl.acm.org/doi/10.1007/11550518_15

View Options

View options

Media

Figures

Other

Tables

View Table of Contents