Study of clustering algorithms for temporal segmentation of egocentric image sequences
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/158176
comunitat-uji-handle2:10234/71345
comunitat-uji-handle3:10234/94547
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Títol
Study of clustering algorithms for temporal segmentation of egocentric image sequencesAutoria
Tutor/Supervisor; Universitat.Departament
Traver Roig, Vicente Javier; Universitat Jaume I. Departament de Llenguatges i Sistemes InformàticsData de publicació
2018-07Editor
Universitat Jaume IResum
In this project, we propose to explore an algorithm to perform an efficient low-level temporal
segmentation on egocentric image sequences. Specifically, the Warped K-Means (WKM) algorithm
was paid a special attention ... [+]
In this project, we propose to explore an algorithm to perform an efficient low-level temporal
segmentation on egocentric image sequences. Specifically, the Warped K-Means (WKM) algorithm
was paid a special attention as it is an adaptation to sequential data of the widely known k-means.
With the ultimate objective of first-person image sequences summarization in mind, this algorithm
is applied to segment the video into low-level events. After a preliminary analysis of the WKM, a
leader-based grouping algorithm is tested on the same data and for the same purpose, to evaluate
its interest for the problem at hand. Contrary to the WKM, the leader-based algorithm requires a
distance threshold to be specified instead of the number of clusters, which can be arguably easier
to set. The leader-based algorithm is first applied directly to our image sequences in order to group
frames corresponding to the same “event”, before being combined with the WKM to address this
problem. Experimental results over six image sequences from two different egocentric datasets
show the efficiency of the method for low-level segmentation. [-]
Paraules clau / Matèries
Descripció
Treball final de Màster Universitari en Sistemes Intel.ligents (Pla de 2013). Codi: SIE043. Curs acadèmic 2017-2018
Tipus de document
info:eu-repo/semantics/masterThesisDrets d'accés
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