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Scene classification using class-supervised local-space-constraint latent Dirichlet allocation
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AI
DOI：10.1007/s11042-015-3024-4.png)
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Abstract
En 中文
This paper proposes a graphical model termed as Local-Space-Constraint LDA (LSC-LDA) for image classification. The existing LDA based methods using the Bag-of-Words (BoW) representation ignore the spatial information of the image. To address this problem, the image is partitioned into several regions and a latent variable is assigned to each region. We construct the supervised LSC-LDA termed as Class-Supervised LSC-LDA (CS-LSC-LDA) to learn class-specific topics. During the parameter learning step, the variational inference is employed to approximate the proposed model. The maximum a posterior probability (MAP) measure is used to compute the parameters. The effectiveness of the proposed model is demonstrated through the extensive evaluations in three well-known datasets. It observes that our model outperforms the existing LDA based models.
Keywords:
Topic model
Classification
LSC-LDA
Location