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Adaptive local hyperplanes for MTV affective analysis

Published: 30 December 2010 Publication History

Abstract

Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective features to high-level emotions by applying machine learning methods. Therefore, choosing effective features and developing efficient machine learning algorithms become vital for affective analysis. In this paper, we investigate the effectiveness of a novel classification approach, called Adaptive Local Hyperplanes (ALH), in affective analysis. The reason ALH is appealing in affective analysis is two-fold. Firstly, affective features are not equally important for emotion categories; ALH inherently assigns feature weights based on discriminative ability of each feature. Secondly, ALH achieves competitive performance with state-of-the-art classifiers (e.g., SVM) while it is designed for multi-class classification. Consequently, it is worthwhile to explore the usage of ALH in affective analysis. MTV data are used in this study. As the first effort of applying ALH to affective analysis, the results presented in this paper provide a foundation for future research in affective analysis.

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cover image ACM Other conferences
ICIMCS '10: Proceedings of the Second International Conference on Internet Multimedia Computing and Service
December 2010
218 pages
ISBN:9781450304603
DOI:10.1145/1937728
  • General Chairs:
  • Yong Rui,
  • Klara Nahrstedt,
  • Xiaofei Xu,
  • Program Chairs:
  • Hongxun Yao,
  • Shuqiang Jiang,
  • Jian Cheng
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 30 December 2010

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