NDLI: Total Cluster: A person agnostic clustering method for broadcast videos

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Total Cluster: A person agnostic clustering method for broadcast videos

Content Provider	ACM Digital Library
Author	Rahtu, Esa Sommerlade, Eric Stiefelhagen, Rainer Tapaswi, Makarand Zisserman, Andrew Parkhi, Omkar M.
Abstract	The goal of this paper is unsupervised face clustering in edited video material – where face tracks arising from different people are assigned to separate clusters, with one cluster for each person. In particular we explore the extent to which faces can be clustered automatically without making an error. This is a very challenging problem given the variation in pose, lighting and expressions that can occur, and the similarities between different people. The novelty we bring is three fold: first, we show that a form of weak supervision is available from the editing structure of the material – the shots, threads and scenes that are standard in edited video; second, we show that by first clustering within scenes the number of face tracks can be significantly reduced with almost no errors; third, we propose an extension of the clustering method to entire episodes using exemplar SVMs based on the negative training data automatically harvested from the editing structure. The method is demonstrated on multiple episodes from two very different TV series, Scrubs and Buffy. For both series it is shown that we move towards our goal, and also outperform a number of baselines from previous works.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450330619
DOI	10.1145/2683483.2683490
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-12-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Video-editing structure Tv shows Face track clustering
Content Type	Text
Resource Type	Article

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