Social Image Analysis From a Non-IID Perspective

An image in social media, termed a social image, exhibits characteristics different from images widely discussed in image processing. They can be described by both content and social related attributes, called social image attributes, including visual contents, users, tags, and timestamps. There are strong coupling relationships between social image attributes, which make social images not independent and identically distributed (non-IID). By analyzing the relationships among these attributes, we can better understand the semantic activities conducted on such non-IID social images, hence enabling new applications including content organization, recommendation, and social activity understanding. In this article, we present a novel algorithm to analyze the coupling relationships between social images, which involves not only intra-coupled similarity within a social image attribute, but also inter-coupled similarity between attributes, in analyzing the non-IIDness of the similarity between social images. In particular, we propose a multi-entry version of the coupled similarity metric to deal with attributes (i.e., tags) which have a many-to-one relationship with respect to images. Experimental results on a Flickr group dataset show that the proposed algorithm captures coupling relationships and therefore achieves promising results in various applications, including image clustering and tagging.