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A feature binding computational model for multi-class object categorization and recognition

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Abstract

We propose a feature binding computational model based on the cognitive research findings. Feature integration theory is widely approved on the principles of the binding problem, which supplies the roadmap for our computational model. We construct the learning procedure to acquire necessary pre-knowledge for the recognition network on reasonable hypothesis–maximum entropy. With the recognition network, we bind the low-level image features with the high-level knowledge. Fundamental concepts and principles of conditional random fields are employed to model the feature binding process. We apply our model to current challenging problems, multi-label image classification and object recognition, and evaluate it on the benchmark image databases to demonstrate that our model is competitive to the state-of-the-art method.

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Acknowledgments

We would like to thank Shutton et al. for their open source code of their work in [13]. Our work is supported by the National Basic Research Priorities Programme (No. 2007CB311004), National Science and Technology Support Plan (No. 2006BAC08B06), and National Science Foundation of China (No.60775035, No. 60903141, No. 60933004, No. 60970088).

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Authors and Affiliations

  1. The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Graduate University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100190, China

    Xishun Wang, Xi Liu, Zhongzhi Shi & Hongjian Sui

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  1. Xishun Wang
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  2. Xi Liu
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  4. Hongjian Sui
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Correspondence to Xishun Wang.

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Wang, X., Liu, X., Shi, Z. et al. A feature binding computational model for multi-class object categorization and recognition. Neural Comput & Applic 21, 1297–1305 (2012). https://doi.org/10.1007/s00521-011-0562-1

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  • DOI: https://doi.org/10.1007/s00521-011-0562-1

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