Search Machine Learning Repository:
Non-Linear Stationary Subspace Analysis with Application to Video Classification
Authors: Mahsa Baktashmotlagh, Mehrtash Harandi, Abbas Bigdeli, Brian Lovell and Mathieu Salzmann
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
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.
authors venues years
Suggest Changes to this paper.
Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).