Robust diffusion-based unsupervised object labelling in distributed camera networks

Teklehaymanot, Freweyni K.; Muma, Michael; Béjar, Benjamín; Binder, Patricia; Zoubir, A.M.; Vetterli, Martin

doi:10.1109/afrcon.2015.7331863

Cited by 13 publications

(14 citation statements)

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“…Each class is described by a number of application dependent descriptive statistics (features). The feature estimation process is an application-specific research area of its own (see, e.g., [8,9]) and is not considered in this article, where we seek for generic adaptive robust clustering and classification methods. In the following, it is assumed that the feature extraction has already been performed, so that the uncertainty of the feature estimation within each class can be modeled by a probability distribution, e.g., the Gaussian.…”

Section: Problem Formulation and Data Modelmentioning

confidence: 99%

“…This property makes the DKM difficult to use in real-time applications where an observation needs to be classified based on streaming data, such as for example in speaker labeling for MDMT speech enhancement [2] or object labeling in MDMT video enhancement for camera networks [9]. In addition to that, the performance of the DKM is limited in scenarios where feature vectors contain outliers.…”

Section: End Formentioning

confidence: 99%

“…In particular, object or speaker labeling can be solved by in-network adaptive classification algorithms where a minimum amount of information is exchanged among single-hop neighbors. Various methods have been proposed that deal with distributed data clustering and classification, e.g., [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. In the last few years, several distributed adaptive strategies, such as incremental, consensus, and diffusion least mean squares algorithms have been developed [25].…”

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confidence: 99%

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Robust and adaptive diffusion-based classification in distributed networks

Binder

Muma

Zoubir

2016

EURASIP J. Adv. Signal Process.

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Distributed adaptive signal processing and communication networking are rapidly advancing research areas which enable new and powerful signal processing tasks, e.g., distributed speech enhancement in adverse environments. An emerging new paradigm is that of multiple devices cooperating in multiple tasks (MDMT). This is different from the classical wireless sensor network (WSN) setup, in which multiple devices perform one single joint task. A crucial first step in order to achieve a benefit, e.g., a better node-specific audio signal enhancement, is the common unique labeling of all relevant sources that are observed by the network. This challenging research question can be addressed by designing adaptive data clustering and classification rules based on a set of noisy unlabeled sensor observations. In this paper, two robust and adaptive distributed hybrid classification algorithms are introduced. They consist of a local clustering phase that uses a small part of the data with a subsequent, fully distributed on-line classification phase. The classification is performed by means of distance-based similarity measures. In order to deal with the presence of outliers, the distances are estimated robustly. An extensive simulation-based performance analysis is provided for the proposed algorithms. The distributed hybrid classification approaches are compared to a benchmark algorithm where the error rates are evaluated in dependence of different WSN parameters. Communication cost and computation time are compared for all algorithms under test. Since both proposed approaches use robust estimators, they are, to a certain degree, insensitive to outliers. Furthermore, they are designed in a way that they are applicable to on-line classification problems.

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Section: Problem Formulation and Data Modelmentioning

confidence: 99%

Section: End Formentioning

confidence: 99%

mentioning

confidence: 99%

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Robust and adaptive diffusion-based classification in distributed networks

Binder

Muma

Zoubir

2016

EURASIP J. Adv. Signal Process.

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“…A crucial first step towards this so-called multiple devices multiple tasks (MDMT) paradigm is to answer the question: who observes what? [6]- [9]. For example, to arrive at a node-specific speech signal enhancement [10]- [12], all relevant speech sources must be uniquely labelled throughout the wireless acoustic sensor network.…”

Section: Introductionmentioning

confidence: 99%

Gravitational Clustering: A simple, robust and adaptive approach for distributed networks

2018

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Distributed signal processing for wireless sensor networks enables that different devices cooperate to solve different signal processing tasks. A crucial first step is to answer the question: who observes what? Recently, several distributed algorithms have been proposed, which frame the signal/object labelling problem in terms of cluster analysis after extracting sourcespecific features, however, the number of clusters is assumed to be known. We propose a new method called Gravitational Clustering (GC) to adaptively estimate the time-varying number of clusters based on a set of feature vectors. The key idea is to exploit the physical principle of gravitational force between mass units: streaming-in feature vectors are considered as mass units of fixed position in the feature space, around which mobile mass units are injected at each time instant. The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i.e., gravitational clusters. By sharing estimates among neighboring nodes via a diffusion-adaptation scheme, cooperative and distributed cluster enumeration is achieved. Numerical experiments concerning robustness against outliers, convergence and computational complexity are conducted. The application in a distributed cooperative multi-view camera network illustrates the applicability to real-world problems.Index Terms-adaptive distributed clustering, cluster enumeration, robust, outlier, multi device multi task (MDMT), wireless sensor networks, labelling.

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“…Traditionally, the design of distributed algorithms has focused on networks where the nodes observe the same phenomenon and/or are interested in the same network-wide signal processing task [1]- [3]. However, motivated by the heterogeneity of today's digital networks, recent advances in distributed adaptive signal processing and communication networking are currently enabling a novel paradigm where the networks are formed by Multiple Devices cooperating in Multiple Tasks (MDMT) [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-task wireless acoustic sensor network for node-specific speech enhancement and DOA estimation

Hassani

Plata-Chaves

Bertrand

et al. 2016

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)

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We consider the design of a distributed algorithm that is suitable for a wireless acoustic sensor network formed by nodes solving multiple tasks (MDMT). In the network, some of the nodes aim at estimating the node-specific direction-of-arrival of some desired sources. Additionally, there are other nodes that aim at implementing either a multi-channel Wiener filter or a minimum variance distortionless response beamformer in order to estimate node-specific desired signals as they impinge on their microphones. By using compressive filter-and-sum operations that incorporate a low-rank approximation of the sensor signal correlation matrix, the proposed MDMT algorithm let the nodes cooperate to achieve the network-wide centralized solution of their node-specific estimation problems without any knowledge about the tasks of other nodes. Finally, the effectiveness of the algorithm is shown through computer simulations.Index Terms-Distributed node-specific signal estimation, subspace estimation, wireless acoustic sensor networks.

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Robust diffusion-based unsupervised object labelling in distributed camera networks

Cited by 13 publications

References 13 publications

Robust and adaptive diffusion-based classification in distributed networks

Robust and adaptive diffusion-based classification in distributed networks

Gravitational Clustering: A simple, robust and adaptive approach for distributed networks

Multi-task wireless acoustic sensor network for node-specific speech enhancement and DOA estimation

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