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Biologically Motivated Model for Outdoor Scene Classification

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Abstract

This paper focuses on the problem of scene classification for mobile robots in an outdoor environment. We present a novel model that combines biologically inspired features and cortex-like memory patterns. The biologically inspired gist feature is used to characterize the content of a scene image. The Incremental Hierarchical Discriminant Regression tree is used to simulate the generation and recall process of human memory. The association between the gist feature and the scene label is established in an incremental way. A cognitive model of the world is constructed using real-time online learning, and a new scene differentiated by reasoning. Using the biologically motivated model, we solved the outdoor scene classification problem on the University of Southern California data set. Experimental results indicate the incremental model improves the classification accuracy rates to nearly 100 % and significantly reduces training costs compared with other biologically inspired feature-based approaches. The new scene classification system achieves state-of-the-art performance.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61105031). Sincere gratitude from the authors go to Weng and his group for the material and code of “Incremental Hierarchical Discriminant Regression” and Itti and his group for providing the data set.

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Authors and Affiliations

  1. School of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, People’s Republic of China

    Jingjing Zhao, Chun Du, Hao Sun, Xingtong Liu & Jixiang Sun

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  1. Jingjing Zhao
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  2. Chun Du
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  3. Hao Sun
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  4. Xingtong Liu
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  5. Jixiang Sun
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Correspondence to Jingjing Zhao.

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Zhao, J., Du, C., Sun, H. et al. Biologically Motivated Model for Outdoor Scene Classification. Cogn Comput 7, 20–33 (2015). https://doi.org/10.1007/s12559-013-9227-y

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