Special issue on "visual semantic analysis with weak supervision"
image segmentation, photo cropping, video summarization, and so on. This special issue also targets on applying new types of weak supervision in visual semantic modeling, e.g. image retrieval based on user feedback and interactive image rendering. Submissions came from an open call for paper, and finally, more than ten papers are selected after rigorous reviews. These papers cover wide applications based on weakly supervised graph inference, weakly supervised feature selection, image retrieval using weak labels, and so on. A brief introduction of some representative papers is as follows: In "A Discriminative Graph Inferring Framework towards Weakly Supervised Image Parsing" Yu et al. introduced a unified Discriminative Graph Inferring (DGI) framework by simultaneously inferring patch labels and learning the patch appearance models. The graph is constructed by connecting the nearest neighbors which share the same image label, and multiple correlations among patches and image labels are imposed as constraints to the inferring. For each label, the patches which do not contain the target label are adopted as negative samples to learn the appearance model. In this way, the predicted labels will be more accurate in the propagation. Graph inferring and the learned patch appearance models are finally embedded to complement each other in one unified formulation. In "3D Object Retrieval based on Viewpoint Segmentation" Leng et al. proposed a normal method for view segmentation, based on Markov random field (MRF) model, which consider not only the difference between the content of views but also the relative locations. Each view is obtained by projecting at certain view points and angels; therefore, these locations can be applied to depict each view, with content of views. The authors use the MRF to implement view segmentation and choose the Every day, the volume of visual data that we handle is increasing exponentially due to the various factors, i.e. the availability of ubiquitous and cheap sensors, sharing platforms, and social trends. In this circumstance, effectively managing the large amount visual data is becoming an important yet challenging research topic. Artificial intelligence has been proven to be effective for interpreting and managing this preponderance of data. It is generally accepted that a tough challenge in managing the large scale visual data is the "semantic gap" problem. To effectively fill the semantic gap of these visual media, weakly supervised learning paradigms are developed, focusing on an intelligent mechanism that transfers the image/video level semantics to different regions. Compared with the laborintensive labeling in fully supervised setting, the transferring mechanism can greatly reduce the human effort, especially given the tremendous visual data on the Websites. Extensive research efforts have been dedicated to weakly supervised learning the visual semantics, while effective tools to manipulate these data are still at their infancy. This special issue will target the most recent technical progresses on learning techniques for visual semantic understanding with weak supervision, such as weakly labeled
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