Abstract
How to encode and represent a scene remains a critical problem in both human and computer vision. Traditional local and global features are useful and have some successes; however, many observations on human scene perception seem to point to an object-based representation. In this paper, we propose a high-level representation for scene categorization. First, we utilize semantic segmentation to get semantic regions. Then we obtain an object histogram representation of a scene by summation pooling over all regions. Second, we build spatial and geometrical priors for each object and each pair of co-occurrent objects from training scenes, and integrate the spatial and geometrical information of objects into the scene representation. Experimental results on two datasets demonstrate that the proposed representation is effective and competitive.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under Project 61175116, the Science and Technology Commission of Shanghai Municipality under research grant no. 14DZ2260800 and Shanghai Knowledge Service Platform for Trustworthy Internet of Things (No. ZF1213).
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Luo, X., Xu, J. (2016). Object-Based Representation for Scene Classification. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_14
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DOI: https://doi.org/10.1007/978-3-319-34111-8_14
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