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Building visual memories of video streams

Building visual memories of video streams

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A real-time method that automatically creates a visual memory of a scene using the growing neural gas (GNG) algorithm is described. The memory consists of a graph where nodes encode the visual information of a video stream as a limited set of representative images. GNG nodes are automatically generated and dynamically clustered. This method could be employed by robotic platforms in exploratory and rescue missions.

References

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      • Green M.: Eyewitness memory is unreliable, http://www.visualexpert.com/Resources/eyewitnessmemory.html.
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      • Donatti, G.S., Wurtz, R.P.: `Using growing neural gas networks to represent visual object knowledge', 21stInt. Conf. Tools with Artificial Intelligence, 2009, (ICTAI '09)., November 2009, p. 54–58.
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      • Garcá-Rodrguez, J., Flórez-Revuelta, F., Garcá-Chamizo, J.M.: `Hybrid gng architecture learns features in images', Proc. 3rd Int. Workshop on Hybrid Artificial Intelligence Systems, (HAIS '08), 2008, Heidelberg, Berlin, Springer-Verlag, p. 451–457.
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      • K.N. Ngan , H. Li . (2011) Video segmentation and its applications.
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