Challenging issues in visual information understanding researches

Abstract

Visual information understanding is known as one of the most difficult and challenging problems in the realization of machine intelligence. This paper presents research issues and overview of the current state of the art in the general flow of visual information understanding. In general, the first stage of the visual understanding starts from the object segmentation. Using the saliency map based on human visual attention model is one of the most promising methods for object segmentation. The next step is scene understanding by analyzing semantics between objects in a scene. This stage finds description of image data with a formatted text. The third step requires space understanding and context awareness using multi-view analysis. This step helps solving general occlusion problem very easily. The final stage is time series analysis of scenes and a space. After this stage, we can obtain visual information from a scene, a series of scenes, and space variations. Various technologies for visual understanding already have been tried and some of them are matured. Therefore, we need to leverage and integrate those techniques properly from the perspective of higher visual information understanding.