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Proceedings Paper

A combined feature latent semantic model for scene classification
Author(s): Yue Jiang; Runsheng Wang
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Paper Abstract

Due to vast growth of image databases, scene image classification methods have become increasingly important in computer vision areas. We propose a new scene image classification framework based on combined feature and a latent semantic model which is based on the Latent Dirichlet Allocation (LDA) in the statistical text literature. Here the model is applied to visual words representation for images. We use Gibbs sampling for parameter estimation and use several different numbers of topics at the same time to obtain the latent topic representation of images. We densely extract multi-scale patches from images and get the combined feature on these patches. Our method is unsupervised. It can also well represent semantic characteristic of images. We demonstrate the effectiveness of our approach by comparing it to those used in previous work in this area. Experiments were conducted on three often used image databases, and our method got better results than the others.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749608 (30 October 2009); doi: 10.1117/12.832619
Show Author Affiliations
Yue Jiang, National Univ. of Defense Technology (China)
Runsheng Wang, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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