Time-series Clustering with Jointly Learning Deep Representations, Clusters and Temporal Boundaries

@article{Tzirakis2019TimeseriesCW,
  title={Time-series Clustering with Jointly Learning Deep Representations, Clusters and Temporal Boundaries},
  author={Panagiotis Tzirakis and Mihalis A. Nicolaou and Bj{\"o}rn W. Schuller and Stefanos Zafeiriou},
  journal={2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)},
  year={2019},
  pages={1-5}
}
Clustering and segmentation of temporal data is an important task across several fields, with prominent applications in computer vision and machine learning such as face and gesture segmentation. Several related methods have been proposed in literature, focusing on learning temporal boundaries and clusters, with recent works focusing on learning deep representations for clustering. However, none of the proposed methods is suitable for jointly learning segments, clusters, as well as… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 28 REFERENCES

Aligned Cluster Analysis for temporal segmentation of human motion

  • 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
  • 2008
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

Joint Unsupervised Learning of Deep Representations and Image Clusters

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

A Proximity-Aware Hierarchical Clustering of Faces

  • 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
  • 2017
VIEW 1 EXCERPT