Tetsu Matsukawa, Takio Kurita


This paper presents scene classification methods using spatial relationship between local posterior probabilities of each category. Recently, the authors proposed the probability higher-order local autocorrelations (PHLAC) feature. This method uses autocorrelations of local posterior probabilities to capture spatial distributions of local posterior probabilities of a category. Although PHLAC achieves good recognition accuracies for scene classification, we can improve the performance further by using crosscorrelation between categories. We extend PHLAC features to crosscorrelations of posterior probabilities of other categories. Also, we introduce the subtraction operator for describing another spatial relationship of local posterior probabilities, and present vertical/horizontal mask patterns for the spatial layout of auto/crosscorrelations. Since the combination of category index is large, we compress the proposed features by two-dimensional principal component analysis. We confirmed the effectiveness of the proposed methods using Scene-15 dataset, and our method exhibited competitive performances to recent methods without using spatial grid informations and even using linear classifiers.


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Paper Citation

in Harvard Style

Matsukawa T. and Kurita T. (2010). SCENE CLASSIFICATION USING SPATIAL RELATIONSHIP BETWEEN LOCAL POSTERIOR PROBABILITIES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 325-332. DOI: 10.5220/0002819903250332

in Bibtex Style

author={Tetsu Matsukawa and Takio Kurita},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
SN - 978-989-674-029-0
AU - Matsukawa T.
AU - Kurita T.
PY - 2010
SP - 325
EP - 332
DO - 10.5220/0002819903250332