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Proceedings Paper

Scene classification using low-level feature and intermediate feature
Author(s): Pu Zeng; Jun Wen; Ling-Da Wu
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Paper Abstract

This paper presents a novel scene classification method using low-level feature and intermediate feature. The purpose of the proposed method is to improve the performance of scene classification and reduce the labeled data required using the complementary information between low-level and intermediate feature. The proposed method uses the co-training algorithm to classify scenes, in which the low-level feature and intermediate feature are two views of co-training algorithm. For low-level feature, Block Based Gabor Texture (BBGT) feature is extracted to describe the texture property of images incorporating the spatial layout information. For intermediate feature, Bag Of Word (BOW) feature is extracted to describe the distribution of local semantic concepts in images based on quantized local descriptors. Experiment results show that this proposed method has satisfactory classification performances on a large set of 13 categories of complex scenes.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678807 (15 November 2007); doi: 10.1117/12.750586
Show Author Affiliations
Pu Zeng, National Univ. of Defense Technology (China)
Jun Wen, National Univ. of Defense Technology (China)
Ling-Da Wu, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision

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