Multimodal Diaries

Torre, Fernando De la; Agell, Carlos

doi:10.1109/icme.2007.4284781

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Unusual facial expressions can be detected through the analysis of outlying temporal patterns. De la Torre and Agell [24] decomposed a multimodal stream of human behavior into several activities using semi-supervised temporal clustering. Recently, Guerra-Filho and Aloimonos [8], [25] presented a linguistic framework for modeling and learning human activity representations from video.…”

Section: Human Motion Analysismentioning

confidence: 99%

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Zhou

Torre

Hodgins

2013

IEEE Trans. Pattern Anal. Mach. Intell.

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302

186

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Abstract-Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as a temporal clustering one, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for the time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

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Section: Human Motion Analysismentioning

confidence: 99%

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Zhou

Torre

Hodgins

2013

IEEE Trans. Pattern Anal. Mach. Intell.

Self Cite

302

186

View full text Add to dashboard Cite

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“…Mustlink constraints are set between all pairs of observations, and a fixed penalty is inflicted when the following conditions are fulfilled simultaneously: the observations are not assigned to the same cluster and the time difference between their timestamps is less than a certain threshold. A similar technique is used in [11], where constraints are used to penalize nonsmooth changes (over time) on the assigned clusters. This segmenting technique is used to detect tasks performed during a day, based on video, on sound and on GPS information.…”

Section: Related Workmentioning

confidence: 99%

Structuring Typical Evolutions Using Temporal-Driven Constrained Clustering

Rizoiu¹,

Velcin²,

Lallich³

2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

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Abstract-In this paper, we propose a new time-aware dissimilarity measure that takes into account the temporal dimension. Observations that are close in the description space, but distant in time are considered as dissimilar. We also propose a method to enforce the segmentation contiguity, by introducing, in the objective function, a penalty term inspired from the Normal Distribution Function. We combine the two propositions into a novel time-driven constrained clustering algorithm, called TDCK-Means, which creates a partition of coherent clusters, both in the multidimensional space and in the temporal space. This algorithm uses soft semi-supervised constraints, to encourage adjacent observations belonging to the same entity to be assigned to the same cluster. We apply our algorithm to a Political Studies dataset in order to detect typical evolution phases. We adapt the Shannon entropy in order to measure the entity contiguity, and we show that our proposition consistently improves temporal cohesion of clusters, without any significant loss in the multidimensional variance.

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ClusPath: a temporal-driven clustering to infer typical evolution paths

Rizoiu

Velcin

Bonnevay

et al. 2015

Data Min Knowl Disc

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We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of entities. We show how abstract notions, such as the Swedish socio-economical model (in a political dataset) or the companies fiscal optimization (in an economical dataset) can be inferred from low-level descriptive features. Such high-level regularities in the evolution of entities are detected by combining spatial and temporal features into a spatio-temporal dissimilarity measure and using semi-supervised clustering techniques. The relations between the evolution phases are modeled using a graph structure, inferred simultaneously with the partition, by using a "slow changing world" assumption. The idea is to ensure a smooth passage for entities along their evolution paths, which catches the long-term trends in the dataset. Additionally, we also provide a method, based on an evolutionary algorithm, to tune the parameters of ClusPath to new, unseen datasets. This method assesses the fitness of a solution using four opposed quality measures and proposes a balanced compromise.Responsible editors: Joao Gama, Indre Zliobaite , Alipio Jorge, Concha Bielza. Electronic supplementary materialThe online version of this article (

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Multimodal Diaries

Cited by 5 publications

References 5 publications

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Structuring Typical Evolutions Using Temporal-Driven Constrained Clustering

ClusPath: a temporal-driven clustering to infer typical evolution paths

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