Abstract
This paper proposes an artificial visual attention model which builds a saliency map associated to the sensed scene using a novel perception-based grouping process. This grouping mechanism is performed by a hierarchical irregular structure, and it takes into account colour contrast, edge and depth information. The resulting saliency map is composed by different parts or ‘pre-attentive objects’ which correspond to units of visual information that can be bound into a coherent and stable object. Besides, the ability to handle dynamic scenarios is included in the proposed model by introducing a tracking mechanism of moving objects, which is also performed using the same hierarchical structure. This allows to conduct the whole attention mechanism in the same structure, reducing the computational time. Experimental results show that the performance of the proposed model is compatible with the existing models of visual attention whereas the object-based nature of the proposed approach renders advantages of precise localization of the focus of attention and proper representation of the shapes of the attended ‘pre-attentive objects’.
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
Aziz, M.Z., Mertsching, B.: Color saliency and inhibition using static and dynamic scenes in region based visual attention. In: Paletta, L., Rome, E. (eds.) WAPCV 2007. LNCS (LNAI), vol. 4840, pp. 234–250. Springer, Heidelberg (2007)
Eriksen, C.W., Yenh, Y.Y.: Allocation of attention in the visual field. Journal of Experimental Psychology: Human Perception and Performance 11(5), 583–597 (1985)
Koch, C., Ullman, S.: Shifts selective visual attention: Towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)
Milanese, R.: Detecting salient regions in an image: from biological evidence to computer implementation, PhD Thesis, Univ. of Geneva (1993)
Itti, L.: Real-time high-performance attention focusing in outdoors color video streams. In: Proc. SPIE Human Vision and Electronic Imaging (HVEI 2002), pp. 235–243 (2002)
Treisman, A.M., Gelade, G.: A feature integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)
Maki, A., Nordlund, P., Eklundh, J.O.: Attentional scene segmentation: integrating depth and motion. Computer Vision and Image Understanding 78(3), 351–373 (2000)
Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans. on Image Processing 13(10), 1304–1318 (2004)
Sun, Y., Fisher, R.B.: Object-based visual attention for computer vision. Artificial Intelligence 146(1), 77–123 (2003)
Orabona, F., Metta, G., Sandini, G.: A proto-object based visual attention model. In: Paletta, L., Rome, E. (eds.) WAPCV 2007. LNCS (LNAI), vol. 4840, pp. 198–215. Springer, Heidelberg (2007)
Pylyshyn, Z.W.: Visual indexes, preconceptual objects, and situated vision. Cognition 80(1–2), 127–158 (2001)
Backer, G., Mertsching, B.: Two selection stages provide efficient object-based attentional control for dynamic vision. In: Paletta, L. (ed.) International Workshop on Attention and Performance in Computer Vision (WAPCV 2003). Joanneum Research, Graz (2003)
Marfil, R., Molina-Tanco, L., Bandera, A., Sandoval, F.: The construction of bounded irregular pyramids with a union-find decimation process. In: Escolano, F., Vento, M. (eds.) GbRPR 2007. LNCS, vol. 4538, pp. 307–318. Springer, Heidelberg (2007)
Marfil, R., Molina-Tanco, L., Rodríguez, Sandoval, F.: Real-time object tracking using bounded irregular pyramids. Pattern Recognition Letters 28, 985–1001 (2007)
Marfil, R., Molina-Tanco, L., Bandera, A., Rodríguez, J.A., Sandoval, F.: Pyramid segmentation algorithms revisited. Pattern Recognition 39(8), 1430–1451 (2006)
Huart, J., Bertolino, P.: Similarity-based and perception-based image segmentation. In: Proc. IEEE Int. Conf. on Image Processing, vol. 3(3), pp. 1148–1151 (2005)
Itti, L., Koch, U., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)
Frintrop, S., Backer, G., Rome, E.: Goal-directed search with a top-down modulated computational attention system. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 117–124. Springer, Heidelberg (2005)
Backer, G., Mertsching, B., Bollmann, M.: Data- and model-driven gaze control for an active-vision system. IEEE Trans. on Pattern Analysis and Machine Intelligence 23, 1415–1429 (2001)
Tipper, S.P.: Object-centred inhibition of return of visual attention. Quarterly Journal of Experimental Psychology 43, 289–298 (1991)
Itti, L., Koch, C.: Feature combination strategies for saliency-based visual attention systems. Journal of Electronic Imaging 10(1), 161–169 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marfil, R., Bandera, A., Rodríguez, J.A., Sandoval, F. (2009). A Novel Hierarchical Framework for Object-Based Visual Attention. In: Paletta, L., Tsotsos, J.K. (eds) Attention in Cognitive Systems. WAPCV 2008. Lecture Notes in Computer Science(), vol 5395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00582-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-00582-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00581-7
Online ISBN: 978-3-642-00582-4
eBook Packages: Computer ScienceComputer Science (R0)