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Local-class-shared-topic latent Dirichlet allocation based scene classification
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DOI：10.1007/s11042-016-3863-7.png)
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Abstract
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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.
Keywords:
Scene classification
Local-class-shared topic
Local-class-specific topic
Graphical model
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