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Scene Classification Using Spatial and Color Features

Abstract : With the increment of images in modern time, scene classification becomes more significant and harder to be settled. Many models have been proposed to classify scene images such as Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). In this paper, we propose an improved method, which combines spatial and color features and bases on PLSA model. When calculating and quantizing spatial features, chain code is used in the process of feature extraction. At the same time, color features are extracted in every block region. The PLSA model is applied in the scene classification. Finally, the experiment results between PLSA and other models are compared. The results show that our method is better than many other state-of-the-art scene classification methods.
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Peilong Zeng, Zhixin Li, Canlong Zhang. Scene Classification Using Spatial and Color Features. 8th International Conference on Intelligent Information Processing (IIP), Oct 2014, Hangzhou, China. pp.259-268, ⟨10.1007/978-3-662-44980-6_29⟩. ⟨hal-01383340⟩



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