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

Simon Jones, Ling Shao

Research output: Contribution to conferenceOtherpeer-review

34 Citations (Scopus)

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.
Original languageEnglish
Pages820-826
Number of pages7
DOIs
Publication statusPublished - 25 Sep 2014
Event2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Columbus, OH, USA
Duration: 23 Jun 201428 Jun 2014

Conference

Conference2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Period23/06/1428/06/14

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