Special issue on contextual vision computing

The popularity of web 2.0 content brings the proliferation of social media in recent years. The intrinsic attributes of social media are to facilitate interactive information sharing, interoperability and collaboration on the internet. By virtue of that, web images and videos are generally accompanied by user-contributed contextual information such as tags, comments, etc. Massive emerging social media data offer new opportunities for resolving the long-standing challenges in computer vision. Fox example, how to jointly represent the visual aspect and user annotation of multimedia data and how can we build video indexing and enable search to benefit from contextual information? So we face both challenges and opportunities in the research on contextual vision computing. This special issue is organized with the purpose of introducing novel research work on contextual vision computing. Submissions are from an open call for paper. With the assistance of professional referees, ten papers out from seventeen submissions are accepted after two rounds of rigorous reviews. These papers cover a wide range of subtopics of contextual vision computing, including visual representation, image classification, tag localization, saliencydetection, pedestrian detection, and so on. The first part of the special issue contains three papers. These papers focus on the image representation, classification and local semantic analysis by directly leveraging user-generated context information. In the first paper, “Semisupervised Unified Latent Factor Learning with Multi-view