Abstract
A novel bionic, middle-semantic object annotation framework is presented in this paper. Moreover, we build the model based on the perception as defined by the human visual system. At first, the super-pixel is used to represent the images, and conditional random field could label each of the super-pixels, which means annotating the different classes of objects. In next step, on the basis of the previous result, image pyramid is used to represent the image, and get the sub-region of some objects of the same class. After extracting descriptor to represent the patches, all the patches are projected to a manifold, which could annotate the different views of objects from the same class. Experiments show that the bionic, middle-semantic object annotation framework could obtain superior results with respect to accuracy, and it could verify the correctness of WordNet indirectly.
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Acknowledgments
This work was financially supported by the Chinese People’s Public Security University Natural Science Foundation (2011LG08).
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Feng, W., Wu, S. (2014). Research on Middle-Semantic Manifold Object Annotation. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_20
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DOI: https://doi.org/10.1007/978-3-642-37835-5_20
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