Scene classification based on spatial pyramid representation by superpixel lattices and contextual visual features

Guanghua Gu; Yao Zhao; Zhenfeng Zhu; Fengcai Li

doi:10.1117/1.OE.51.1.017201

7 February 2012 Scene classification based on spatial pyramid representation by superpixel lattices and contextual visual features

Guanghua Gu, Yao Zhao, Zhenfeng Zhu, Fengcai Li

Author Affiliations +

Funded by: National Natural Science Foundation of China, Natural Science Foundation of China, National Natural Science Funds for Distinguished Young Scholar, National Science Foundation of China for Distinguished Young Scholars, Sino-Singapore JRP, Plan of Science and Technology of Qinhuangdao City, Beijing Natural Science Foundation, Fundamental Research Funds for the Central Universities

Optical Engineering, Vol. 51, Issue 1, 017201 (February 2012). https://doi.org/10.1117/1.OE.51.1.017201

Abstract

Natural scene classification is a challenging open problem in computer vision. We present a novel spatial pyramid representation scheme for recognizing scene category. Initially, each image is partitioned into sub-blocks, applying the technology of superpixel lattices segmentation according to a boosted edge learning boundary map, which makes the objects in each sub-block have the integrity-that is, the features in each sub-block are relatively consistent. Then, we extract the dense scale-invariant feature transform features of the images and form the contextual visual feature description. Finally, the image representations are performed by following the methodology of spatial pyramid. The feature descriptions we present include both local structural information and global spatial structural information; therefore, they are more discriminative for scene classification. Experiments demonstrate that the classification rate can achieve about 87.13% on a set of 15 categories of complex scenes.

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Guanghua Gu, Yao Zhao, Zhenfeng Zhu, and Fengcai Li "Scene classification based on spatial pyramid representation by superpixel lattices and contextual visual features," Optical Engineering 51(1), 017201 (7 February 2012). https://doi.org/10.1117/1.OE.51.1.017201

Published: 7 February 2012

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