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Rate versus synchrony code for human action recognition

Maria-Jose Escobar 1 G. Masson 2 Thierry Viéville 1, 3 Pierre Kornprobst 1
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : We propose a bio-inspired feedforward spiking network modeling two brain areas dedicated to motion (V1 and MT), and we show how the spiking output can be exploited in a computer vision application: action recognition. In order to analyze spike trains, we consider two characteristics of the neural code: mean firing rate of each neuron and synchrony between neurons. Interestingly, we show that they carry some relevant information for the action recognition application. We compare our results to Jhuang et al. (2007) on the Weizmann database. As a conclusion, we are convinced that spiking networks represent a powerful alternative framework for real vision applications that will benefit from recent advances in computational neuroscience
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https://hal.inria.fr/inria-00326588
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Submitted on : Friday, October 3, 2008 - 3:58:44 PM
Last modification on : Thursday, July 1, 2021 - 5:58:02 PM
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Maria-Jose Escobar, G. Masson, Thierry Viéville, Pierre Kornprobst. Rate versus synchrony code for human action recognition. [Research Report] RR-6669, INRIA. 2008, pp.36. ⟨inria-00326588⟩

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