Non-linear stationary subspace analysis with application to video classification

Mahsa Baktashmotlagh, Mehrtash T. Harandi, Abbas Bigdeli, Brian C. Lovell, Mathieu Salzmann

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)

Abstract

Low-dimensional representations are key to the success of many video classification algorithms. However, the commonly-used dimensionality reduction techniques fail to account for the fact that only part of the signal is shared across all the videos in one class. As a consequence, the resulting representations contain instance-specific information, which introduces noise in the classification process. In this paper, we introduce Non-Linear Stationary Subspace Analysis: A method that overcomes this issue by explicitly separating the stationary parts of the video signal (i.e., the parts shared across all videos in one class), from its non-stationary parts (i.e., specific to individual videos). We demonstrate the effectiveness of our approach on action recognition, dynamic texture classification and scene recognition.

Original languageEnglish
Pages1487-1495
Number of pages9
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States of America
Duration: 16 Jun 201321 Jun 2013

Conference

Conference30th International Conference on Machine Learning, ICML 2013
Country/TerritoryUnited States of America
CityAtlanta, GA
Period16/06/1321/06/13

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