Natural Scene Classification Based on Integrated Topic Simplex

Tang YINGJUN  Xu DE  Yang XU  Liu QIFANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.9   pp.1811-1814
Publication Date: 2009/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1811
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
scene classification,  ILTM,  LDA,  CC-LDA,  

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We present a novel model named Integrated Latent Topic Model (ILTM), to learn and recognize natural scene category. Unlike previous work, which considered the discrepancy and common property separately among all categories, Our approach combines universal topics from all categories with specific topics from each category. As a result, the model is implemented to produce a few but specific topics and more generic topics among categories, and each category is represented in a different topics simplex, which correlates well with human scene understanding. We investigate the classification performance with variable scene category tasks. The experiments have shown our model outperforms latent-space methods with less training data.