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.

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

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