Tagged image clustering via topic models
Junjun Cui, Lizhen Liu, Hanshi Wang, Chao Du, Wei Song · 2015
With the rapid growth of tagged images, researchers are now resorting to this high semantic textual information for image clustering, which has showed higher clustering performance compared with conventional methods using the low level visual features. However, how to bridge the gap between the semantic information and the visual information is still an open problem. In this paper, a novel topic model based framework is proposed for tagged image clustering, which consists of three steps. Firstly, the statistics between the visual features and the tag features are calculated to utilize the complementary characteristic between the two sources of information. Then the new tag feature embedded by visual information is extracted as the feature of images. Finally, typical topic model, i.e., Latent Dirichlet Allocation, is applied for image clustering. The proposed method can make full use of the tag and visual information for image clustering. Experimental results on two widely used datasets, i.e., Pascal VOC 2007 and NUS-WIDE Flickr databases, demonstrate the effectiveness of the proposed method.