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2011 IEEE 11th International Conference on Data Mining Workshops (2011)
Vancouver, Canada
Dec. 11, 2011 to Dec. 11, 2011
ISBN: 978-0-7695-4409-0
pp: 847-852
Large-scale outdoor scene classification is an important issue in multimedia information retrieval. In this paper, we propose an efficient scene classification model by integrating outdoor scene image's local features into a set of highly discriminative and less redundant graph lets (i.e., small connected sub graph). Firstly, each outdoor scene image is segmented into a number of regions in terms of its color intensity distribution. And a region adjacency graph (RAG) is defined to encode the geometric property and color intensity distribution of outdoor scene image. Then, the frequent sub-structures are mined statistically from the RAGs corresponding to the training outdoor scene images. And a selecting process is carried out to obtain a set of sub-structures from the frequent ones towards being highly discriminative and low redundant. And these selected sub-structures are used as templates to extract the corresponding graph lets. Finally, we integrate these extracted graph lets by a multi-class boosting strategy for outdoor scene classification. The experimental results on the challenging SUN~\cite{sun} data set and the LHI~\cite{lotus} data set validate the effectiveness of our approach.
outdoor scene images, graphlets, feature selection

M. Song, X. Deng, J. Bu, C. Chen and L. Zhang, "Large-scale Outdoor Scene Classification by Boosting a Set of Highly Discriminative and Low Redundant Graphlets," 2011 IEEE 11th International Conference on Data Mining Workshops(ICDMW), Vancouver, Canada, 2011, pp. 847-852.
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