R-clustering for egocentric video segmentation

Estefanía Talaver*, Marc Bolanos, Mariella Dimiccoli, Maedeh Aghaei, Petia Radeva

*Corresponding author for this work

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In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods.
Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis
Subtitle of host publication7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings
EditorsRoberto Paredes, Jaime S. Cardoso, Xosé M. Pardo
Place of PublicationCham
Number of pages10
ISBN (Electronic)978-3-319-19390-8
ISBN (Print)978-3-319-19389-2
Publication statusPublished - 2015
Externally publishedYes
Event7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015 - Santiago de Compostela, Spain
Duration: 17 Jun 201519 Jun 2015
Conference number: 7

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015
Abbreviated titleIbPRIA
CitySantiago de Compostela


  • Temporal video segmentation
  • Egocentric videos
  • Clustering


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