Locally spatiotemporal saliency representation: the role of independent component analysis

Authors:
Tao Jiang;Xingzhou Jiang
Affiliations:
State Key Lab. of Intelligent Technology and Systems, Tsinghua University, Beijing, China;Department 5, Navy University of Engineering, Wuhan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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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.