In-network adaptive cluster enumeration for distributed classification and labeling. Teklehaymanot, F. K., Muma, M., Liu, J., & Zoubir, A. M. In 2016 24th European Signal Processing Conference (EUSIPCO), pages 448-452, Aug, 2016.
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Paper doi abstract bibtex
Paper doi abstract bibtex A crucial first step for signal processing decentralized sensor networks with node-specific interests is to agree upon a common unique labeling of all observed sources in the network. The knowledge “who observes what” is required, e.g. in node-specific audio or video signal enhancement to form node clusters of common interest. Recently proposed in-network distributed adaptive classification and labeling algorithms assume knowledge on the number of objects (clusters), which is not necessarily available in real-world applications. Thus, we consider the problem of estimating the number of data-clusters in the distributed adaptive network set-up. We propose two distributed adaptive cluster enumeration methods. They combine the diffusion principle, where the nodes share information within their local neighborhood only (without fusion center), with the X-means and the PG-means cluster enumeration. Performance is evaluated via simulations and the applicability of the methods is illustrated using a distributed camera network where moving objects appear and disappear from the Line-of-Sight (LOS) and the number of clusters becomes time-varying.
@InProceedings{7760288,
author = {F. K. Teklehaymanot and M. Muma and J. Liu and A. M. Zoubir},
booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
title = {In-network adaptive cluster enumeration for distributed classification and labeling},
year = {2016},
pages = {448-452},
abstract = {A crucial first step for signal processing decentralized sensor networks with node-specific interests is to agree upon a common unique labeling of all observed sources in the network. The knowledge “who observes what” is required, e.g. in node-specific audio or video signal enhancement to form node clusters of common interest. Recently proposed in-network distributed adaptive classification and labeling algorithms assume knowledge on the number of objects (clusters), which is not necessarily available in real-world applications. Thus, we consider the problem of estimating the number of data-clusters in the distributed adaptive network set-up. We propose two distributed adaptive cluster enumeration methods. They combine the diffusion principle, where the nodes share information within their local neighborhood only (without fusion center), with the X-means and the PG-means cluster enumeration. Performance is evaluated via simulations and the applicability of the methods is illustrated using a distributed camera network where moving objects appear and disappear from the Line-of-Sight (LOS) and the number of clusters becomes time-varying.},
keywords = {audio signal processing;distributed sensors;video signal processing;distributed camera network;distributed adaptive cluster enumeration methods;video signal enhancement;audio signal enhancement;signal processing decentralized sensor networks;distributed classification;in-network adaptive cluster enumeration;Cameras;Clustering algorithms;Signal processing;Labeling;Signal processing algorithms;Convergence;Image color analysis;Distributed Cluster Enumeration;Distributed Classification;Object Labeling;Camera Network;X-means;PG-means;MDMT;Diffusion},
doi = {10.1109/EUSIPCO.2016.7760288},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570256129.pdf},
}Downloads: 0
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