Abstract
The purpose of this paper is to present an approach to locate specific regions in images. The novelty of the approach is the combination of a weighted bottom-up visual attention mechanism with a genetic algorithm optimization running on a computational grid. The visual attention mechanism is based on the model proposed by Itti and Koch [1]. A saliency map indicates the most interesting points in an image using a number of intermediate low level features, which are detected at different scales and orientations. Using the saliency map weights as parameters, the optimization problem is to minimize the number of most salient points needed to locate a set of reference image regions, previously (and manually) labeled as being interesting. Both an objective and subjective evaluation have demonstrated that the proposed approach is more effective when compared to a fixed weight attention mechanism.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Itti, L., Koch., C.: Computational Modeling of Visual Attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)
Tsotsos, J.: Analyzing Vision at the Complexity Level. The Behavioral and Brain Sciences 13(3), 423–445 (1990)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40(10-12), 1489–1506 (2000)
Itti, L., Koch, C.: A comparison of feature combination strategies for saliency-based visual attention systems. In: Proc. SPIE human vision and electronic imaging IV, San Jose, USA, pp. 473–482 (1999)
Itti, L.: Models of Bottom-Up Attention and Saliency. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) Neurobiology of Attention, pp. 576–582. Elsevier, San Diego (2005)
Mardle, S., Pascoe, S.: An overview of genetic algorithms for the solution of optimisation problems. Computers in High Education Economics Review 3(1) (1999)
Stentiford, F.: An evolutionary programming approach to the simulation of visual attention. In: Proc. Congress on Evolutionary Computation, Seoul, Korea, pp. 851–858 (2001)
Treptow, A., Zell, A.: Combining Adaboost learning and evolutionary search to select features for real-time object detection. In: Proc. IEEE Congress on Evolutionary Computation, Portland, USA, pp. 2107–2113 (2004)
Pereira, E., Gomes, H., Florentino, V.: Bottom-up visual attention guided by genetic algorithm optimization. In: IASTED International Conference on Signal and Image Processing, Honolulu, USA (August 2006) (accepted)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)
Siagian, C., Ititi, L.: Biologically-Inspired Face Detection: Non-Brute-Force-Search Approach. In: First IEEE-CVPR International Workshop on Face Processing in Video, June 2004, pp. 62–69 (2004)
Itti, L., Koch, C.: Feature Combination Strategies for Saliency-Based Visual Attention Systems. Journal of Electronic Imaging 10(1), 1–169 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pereira, E.T., Gomes, H.M. (2006). Guiding a Bottom-Up Visual Attention Mechanism to Locate Specific Image Regions Using a Distributed Genetic Optimization. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_26
Download citation
DOI: https://doi.org/10.1007/11892755_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46556-0
Online ISBN: 978-3-540-46557-7
eBook Packages: Computer ScienceComputer Science (R0)