Content Vs Context: How About "Walking Hand-In-Hand" For Image Clustering?

Image clustering has been one of the most important issues in the field of pattern recognition. However, most of existing methods only focus on utilizing either content or context information of images, failing to consider both of them. In fact, the powerful algorithms can be realized by a combination of the rich content and context information. This paper proposes a novel content-context information bottleneck (C2IB) algorithm, which simultaneously explores and exploits the content and context information for discovering image clusters. The "content" describes the intrinsic characteristics contained in each image such as the appearance feature, and the "context" depicts the close correlations between images such as inter-image distance or similarity. Then, we formulate the problem as an information loss function by maximally preserving the content and context information while compressing the images. Finally, we design a new sequential method for the optimization. Experimental results show the superiority of the proposed method.