Motion image segmentation using global criteria and DP

Kobayashi, Takumi; Yoshikawa, Fumito; Otsu, Nobuyuki

doi:10.1109/afgr.2008.4813437

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…In the present paper, the optimal segmentation is defined as the segmentation that maximizes the sum of the squared norms of the segment vectors, i.e., k t k 2 . Since a i is centered, and thus the variance of segment vectors is 1 L t k 2 , the notion of optimality here is similar to the Fisher discriminant criterion in the clustering literature: maximization of the variance between clusters, which is also successfully applied to the motion image segmentation [20]. Given a kernel matrix K, the optimal segmentation T * is defined as follows:…”

Section: Optimal Segmentationmentioning

confidence: 99%

Kernel topic segmentation for informal multi-party meetings and performance degradation caused by insufficient lexicon

Sadohara

2010

2010 IEEE Spoken Language Technology Workshop

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We herein propose a domain-independent topic segmentation algorithm for free-form multi-party meeting recordings. The advantage of the proposed algorithm is that topical and lexical knowledge, which are difficult to adapt to the target meeting before speech recognition and topic segmentation, are not required. For an errorful sequence of phonemes obtained using a continuous phoneme recognizer, the proposed algorithm exhaustively analyzes the occurrence pattern of subsequences of phonemes and partitions the sequence into segments with coherent patterns. An empirical study on the ICSI Meeting Corpus has indicated that it performs comparably to lexicalcohesion-based text segmenters applied to human transcripts. Furthermore, the performance of the text segmenters applied to LVCSR output decreases significantly when keywords are not included in the lexicon. This suggests that, for the purpose of obtaining topical structure, the phoneme sequence segmenter could be more robust than text segmenters with LVCSR.

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Section: Optimal Segmentationmentioning

confidence: 99%

Kernel topic segmentation for informal multi-party meetings and performance degradation caused by insufficient lexicon

Sadohara

2010

2010 IEEE Spoken Language Technology Workshop

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“…The quality of the detection, recognition, or synthesis in these applications greatly depends on the spatial and temporal resolution of motion databases, as well as the complexity of the models. Unsupervised techniques to learn motion primitives from training data have recently attracted the interest of many scientists in computer vision [4], [5], [6], [7], [8], [9], [10], [11], [12] and computer graphics [13], [14], [15], [16], [17], [18], [19], [20]. Fig.…”

mentioning

confidence: 99%

“…Over the last few years, several approaches for unsupervised segmentation of activities have been proposed (see, for example, [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18]). HACA presents several advantages:…”

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confidence: 99%

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Zhou

Torre

Hodgins

2013

IEEE Trans. Pattern Anal. Mach. Intell.

306

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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RETRACTED ARTICLE: Multi-layered multi-exemplar affinity propagation for temporal clustering of human motion

Wang

et al. 2015

Multimed Tools Appl

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The publisher, in agreement with the Editor-in-Chief, has retracted this article because it contains large portions of text that have been duplicated from a previously published article: Wang Z, Wang S, Ji Q (2013) Capturing complex spatio-temporal relations among facial muscles for facial expression recognition. Proc. IEEE Conference Computer Vision Pattern Recognition 3422-3429.The online version of this article contains the full text of the retracted article as electronic supplementary material.

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Motion image segmentation using global criteria and DP

Abstract: We propose methods for segmenting a motion sequence into motion primitives, taking into account temporal constraints (continuity along the time axis

Cited by 3 publications

References 9 publications

Kernel topic segmentation for informal multi-party meetings and performance degradation caused by insufficient lexicon

Kernel topic segmentation for informal multi-party meetings and performance degradation caused by insufficient lexicon

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

RETRACTED ARTICLE: Multi-layered multi-exemplar affinity propagation for temporal clustering of human motion

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