Abstract
We propose a framework to integrate the large scale image data visualization with image classification. The Convolution Neural Network is used to learn the feature vector for an image. A fast algorithm is developed for inter-class similarity measurement. The spectral clustering is implemented to construct a hierarchical visual tree. Instead of the flat classification way, a hierarchical classification is designed according to the visual tree, which is transformed to a path search problem. The path with the maximum joint probability is the final solution. Experimental results on the ILSVRC2010 dataset demonstrate that our method achieves the highest top-1 and top-5 classification accuracy in comparison with 6 state-of-the-art methods.
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Acknowledgements
This work was supported by the National Natural Science Foundations of China under Grants 61373077, 61472334 and 61170179,the Natural Science Foundation of Fujian Province of China Under Grant 2013J01257,the Fundamental Research Funds for the Central Universities under Grant 20720130720,the 2014 national college students’ innovative and entrepreneurial training project, and the Scientific Research Foundation for the Introduction of Talent at Xiamen University of Technology YKJ12023R.
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Lu, C., Qu, Y., Shi, C., Fan, J., Wu, Y., Wang, H. (2015). Hierarchical Learning for Large-Scale Image Classification via CNN and Maximum Confidence Path. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_23
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DOI: https://doi.org/10.1007/978-3-319-24078-7_23
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