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Proceedings Paper

Fast image clustering based on convolutional neural network and binary K-means
Author(s): Shengcai Ke; Yongwei Zhao; Bicheng Li; Zhibing Wu; Xin Liu
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Paper Abstract

Visual features used in state-of-the-art image clustering methods lack of learning, which leads to low representational power. Furthermore, the efficiency of traditional clustering methods is low for large image dataset. So, a fast image clustering method based on convolutional neural network and binary K-means is proposed in this paper. Firstly, a large-scale convolutional neural network is employed to learn the intrinsic implications of training images so as to improve the discrimination and representational power of visual features. Secondly, ITQ hash algorithm is applied to map the high dimensional deep features into low-dimensional hamming space, and multi-index hash table is used to index the initial clustering centers so that the nearest center lookup becomes extremely efficient. Finally, image clustering is accomplished efficiently by binary K-means algorithm. Experimental results of ImageNet-1000 datasets indicate that the expression ability of visual features is effectively improved and the image clustering performance is substantially boosted compared with state-of-the-art methods.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100332E (29 August 2016); doi: 10.1117/12.2244263
Show Author Affiliations
Shengcai Ke, Information Engineering Univ. (China)
Yongwei Zhao, Information Engineering Univ. (China)
Bicheng Li, Information Engineering Univ. (China)
Zhibing Wu, Jiangman Institute of Computing Technology (China)
Xin Liu, Information Engineering Univ. (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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