Combining Online Clustering and Rank Pooling Dynamics for Action Proposals
Autor: | Safia Nait-Bahloul, Roberto J. López-Sastre, F.J. Acevedo-Rodriguez, Nadjia Khatir, Marcos Baptista-Rios |
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Rok vydání: | 2019 |
Předmět: |
Computer science
business.industry Pooling 02 engineering and technology Filter (signal processing) 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Hierarchical clustering Sliding window protocol 0202 electrical engineering electronic engineering information engineering Action recognition Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence business Cluster analysis computer Classifier (UML) 0105 earth and related environmental sciences |
Zdroj: | Pattern Recognition and Image Analysis ISBN: 9783030313319 IbPRIA (1) |
DOI: | 10.1007/978-3-030-31332-6_7 |
Popis: | The action proposals problem consists in developing efficient and effective approaches to retrieve, from untrimmed long videos, those temporal segments which are likely to contain human actions. This is a fundamental task for any video analysis solution, which will struggle to detect activities in a large-scale video collection without the proposals step, needing hence to apply an action classifier at every time location, in a temporal sliding window strategy, a pipeline which is clearly unfeasible. While all previous action proposals solutions are supervised, we introduce here a novel strategy that works in an unsupervised fashion. We rely on an online agglomerative clustering algorithm to build an initial set of proposals/clusters. Then a novel filtering approach is proposed, which uses the dynamics of the proposals discovered by the clustering, to measure their actioness, and proceeds to filter them accordingly. Our experiments show that our model improves the supervised state-of-the-art approaches when the number of proposals is controlled. |
Databáze: | OpenAIRE |
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