Abstract
Locally spatiotemporal salience is defined as the combination of the local contrast salience from multiple paralleling independent spatiotemporal feature channels. The computational model proposed in this paper adopts independent component analysis (ICA) to model the spatiotemporal receptive filed of visual simple cells, then uses the learned independent filters for feature extraction. The ICA-based feature extraction for modelling locally spatiotemporal saliency representation (LSTSR) provides such benefits: (1) valid to use LSTSR directly for locally spatial saliency representation (LSSR) since it includes LSSR as one of its special case; (2) Plausible for space variant sampled dynamic scene; (3) Effective for motion-based scene segmentation.
This research was funded by the State Key Lab. of Intelligent Technology and Systems, Tsinghua University, China, with the help of Prof. Peifa Jia.
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
Nothdurft, H.-C.: Salience from Feature Contrast: Temporal Properties of Saliency Mechanisms. Vision Research 40, 2421–2435 (2000)
Koch, C., Ullman, S.: Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry. Human Neurobiology 4, 219–227 (1985)
Itti, L., Dhavale, L.N., Pighin, F.: Realistic Avatar Eye and Head Animation Using a Neurobiological Model of Visual Attention. SPIE, San Jose (2003)
Ouerhani, N., Hugli, H.: A Model of Dynamic Visual Attention for Object Tracking in Natural Image Sequences. In: Mira, J., Álvarez, J.R. (eds.) IWANN 2003. LNCS, vol. 2686, pp. 702–709. Springer, Heidelberg (2003)
Rosenholtz, R.: A Simple Saliency Model Predicts a Number of Motion Popout Phenomena. Vision Research 39, 3157–3163 (1999)
Miura, K., et al.: Initiation of Smooth Pursuit in Humans Dependence on Target Saliency. Exp. Brain Res. 141, 242–249 (2001)
Wandell, B.A.: The Foundations of Vision. Stanford Press (1995)
Itti, L., Koch, C.: A Saliency-based Search Mechanism for Overt and Covert Shifts of Visual Attention. Vision Research 40, 1489–1506 (2000)
Bolduc, M., Levine, M.D.: A Review of Biologically Motivated Space-variant Data Reduction Models for Robotic Vision. Computer vision and image understanding 69, 170–184 (1998)
Jiang, T., et al.: Biologically Motivated Space Variant Sampling: First Step to Active Vision. In: Proceedings of Asian Conference on Computer Vision, pp. 204–209 (2004)
http://i21www.ira.uka.de/image_sequences , KOGS/IAKS Universität Karlsruhe
Hyvärine, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Trans. on Neural Networks 10, 626–634 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jiang, T., Jiang, X. (2005). Locally Spatiotemporal Saliency Representation: The Role of Independent Component Analysis. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_160
Download citation
DOI: https://doi.org/10.1007/11427391_160
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
eBook Packages: Computer ScienceComputer Science (R0)