Abstract
In this paper, we propose a top-down object biased attention model which is based on human visual attention mechanism integrating feature based bottom-up attention and goal based top-down attention. The proposed model can guide attention to focus on a given target colored object over other objects or feature based salient areas by considering the object color biased attention mechanism. We proposed a growing fuzzy topology ART that plays important roles for object color biased attention, one of which is to incrementally learn and memorize features of arbitrary objects and the other one is to generate top-down bias signal by competing memorized features of a given target object with features of an arbitrary object. Experimental results show that the proposed model performs well in successfully focusing on given target objects, as well as incrementally perceiving arbitrary objects in natural scenes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vecera, S.P.: Toward a biased competition account of object-based segregation and attention. Brain and Mind 1, 353–384 (2000)
Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annual Review of Neuroscience 18, 193–222 (1995)
Navalpakkam, V., Itti, L.: An integrated model of top-down and bottom-up attention for optimal object detection. In: CVPR 2006, pp. 2049–2056 (2006)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19(9), 1395–1407 (2006)
Torralba, A., Oliva, A., Castelhano, M., Henderson, J.M.: Contextual guidance of attention in natural scenes: The role of global features on object search. Psychological Review 113(4), 766–786 (2006)
Won, W.J., Yeo, J., Ban, S.W., Lee, M.: Biologically motivated incremental object perception based on selective attention. Int. J. Pattern Recognition & Artificial Intelligence 21(8), 1293–1305 (2007)
Marsland, S., Shapiro, J., Nehmzow, U.: A self-organising network that grows when required. Neural Networks, Special Issue 15(8-9), 1041–1058 (2002)
Goldstein, E.B.: Sensation and perception, 4th edn. An international Thomson publishing company, USA (1996)
Park, S.J., An, K.H., Lee, M.: Saliency map model with adaptive masking based on independent component analysis. Neurocomputing 49, 417–422 (2002)
Choi, S.B., Jung, B.S., Ban, S.W., Niitsuma, H., Lee, M.: Biologically motivated vergence control system using human-like selective attention model. Neurocomputing 69, 537–558 (2006)
Carpenter, G.A., Grossberg, S., Makuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks 3(5), 698–713 (1992)
ABR Lab. Image database, ftp://abr.knu.ac.kr
Itti’s Lab. Image database, http://ilab.usc.edu/research
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hwang, B., Ban, SW., Lee, M. (2008). Top-Down Object Color Biased Attention Using Growing Fuzzy Topology ART. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-88906-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
Online ISBN: 978-3-540-88906-9
eBook Packages: Computer ScienceComputer Science (R0)