Conference Paper

Large-scale Outdoor Scene Classification by Boosting a Set of Highly Discriminative and Low Redundant Graphlets.

DOI: 10.1109/ICDMW.2011.108 Conference: Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011
Source: DBLP


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.

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    ABSTRACT: Scene understanding is a popular research direction. In this area, many attempts focus on the problem of naming objects in the complex natural scene, and visual semantic integration model (VSIM) is the representative. This model consists of two parts: semantic level and visual level. In the first level, it uses a four-level pachinko allocation model (PAM) to capture the semantics behind images. However, this four-level PAM is inflexible and lacks of considerations of common subtopics that represent the background semantics. To address these problems, we use hierarchical PAM (hPAM) to replace PAM. Since hPAM is flexible, we investigate two variations of hPAM to boost VSIM in this paper. We derive the Gibbs sampler to learn the proposed models. Empirical results validate that our works can obtain better performance than the state-of-the-art algorithms.
    Multimedia Tools and Applications 01/2015; DOI:10.1007/s11042-014-2414-3 · 1.35 Impact Factor

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