Abstract
In this paper, we propose a hierarchical probabilistic model for scene classification. This model infers the local–class–shared and local–class-specific latent topics respectively. Our approach consists of first learning the latent topics from the BoW representation and subsequently, training SVM on the distribution of the latent topics. This approach is compared to that of using traditional graphical models to learn the latent topics and training SVM on the topic distribution. The experiments on a variety of datasets show that the topics learned by our model have higher discriminative power.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China (No. 61525102, 61271289), and by The program for Science and Technology Innovative Research Team for Young Scholars in Sichuan Province, China (No. 2014TD0006).
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Huang, C., Luo, W. & Xie, Y. Local–class–shared–topic latent Dirichlet allocation based scene classification. Multimed Tools Appl 76, 15661–15679 (2017). https://doi.org/10.1007/s11042-016-3863-7
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DOI: https://doi.org/10.1007/s11042-016-3863-7