A Multigraph Representation for Improved Unsupervised/Semi-supervised Learning of Human Actions

Jones, Simon and Shao, Ling (2014) A Multigraph Representation for Improved Unsupervised/Semi-supervised Learning of Human Actions. In: CVPR 2014 - IEEE Conference on Computer Vision and Pattern Recognition, 23rd - 28th June 2014, Columbus, Ohio.

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Abstract

Graph-based methods are a useful class of methods for improving the performance of unsupervised and semi-supervised machine learning tasks, such as clustering or information retrieval. However, the performance of existing graph-based methods is highly dependent on how well the affinity graph reflects the original data structure. We propose that multimedia such as images or videos consist of multiple separate components, and therefore more than one graph is required to fully capture the relationship between them. Accordingly, we present a new spectral method - the Feature Grouped Spectral Multigraph (FGSM) - which comprises the following steps. First, mutually independent subsets of the original feature space are generated through feature clustering. Secondly, a separate graph is generated from each feature subset. Finally, a spectral embedding is calculated on each graph, and the embeddings are scaled/aggregated into a single representation. Using this representation, a variety of experiments are performed on three learning tasks - clustering, retrieval and recognition - on human action datasets, demonstrating considerably better performance than the state-of-the-art.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Clustering, Human Action Recognition, Manifold Learning, Multimedia Retrieval, Spectral Embedding, Unsupervised Learning
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	16 Jun 2015 09:19
Last Modified:	13 Oct 2019 00:37
URI:	http://nrl.northumbria.ac.uk/id/eprint/22926

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