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Erschienen in:
2013 | OriginalPaper | Buchkapitel
q-Gaussian Mixture Models Based on Non-extensive Statistics for Image and Video Semantic Indexing
verfasst von : Nakamasa Inoue, Koichi Shinoda
Erschienen in: Computer Vision – ACCV 2012
Verlag: Springer Berlin Heidelberg
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Gaussian mixture models (GMMs) which extend the bag-of-visual-words (BoW) to a probabilistic framework have been proved to be effective for image and video semantic indexing. Recently, the
q
-Gaussian distribution, which is derived in the non-extensive statistics, has been shown to be useful for representing patterns in many
complex
systems in physics such as fractals and cosmology. We propose
q
-Gaussian mixture models (
q
-GMMs), which are mixture models of
q
-Gaussian distributions, for image and video semantic indexing. It has a parameter
q
to control its tail-heaviness. The long-tailed distributions obtained for
q
> 1 are expected to effectively represent complexly correlated data, and hence, to improve robustness against outliers. In our experiments, our proposed method outperformed the BoW method and achieved 49.4% and 10.9% in Mean Average Precision on the PASCAL VOC 2010 dataset and the TRECVID 2010 Semantic Indexing dataset, respectively.