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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9315, pp. 236–245). Springer Verlag. https://doi.org/10.1007/978-3-319-24078-7_23
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