A Survey of Social Image Colocalization

Social image colocalization locates the objects belonging to the same category from a set of images. Its goal is to explore the consistent relationship among social entities in the social system, which is conducive to promoting the development of social scene understanding. In a social relationship, consistency is easy to be ignored but is useful for many fields, e.g., natural language processing and computer vision. The social image colocalization aims to utilize the consistent relationship to discover the objects belonging to the same category and locate them by rectangle bounding boxes. The consistency is principally reflected in the category of objects, which can be achieved by a similar appearance or a uniform structure. Currently, state-of-the-art colocalization methods mainly focus on three solving strategies: candidate region proposal selection, saliency maps-based methods, and deep descriptor transformation. In this article, we sort out several kinds of the present mainstream methods and provide a comprehensive review of their fundamentals and performance. We expect that the overview of colocalization would be helpful for the researchers who are new to this area and provide them enlightenments.