Modeling Visual Rhetoric and Semantics in Multimedia

Recent advances in machine learning have enabled computer vision algorithms to model complicated visual phenomena with accuracies unthinkable a mere decade ago. Their high-performance on a plethora of vision-related tasks has enabled computer vision researchers to begin to move beyond traditional visual recognition problems to tasks requiring higher-level image understanding. However, most computer vision research still focuses on describing what images, text, or other media literally portrays. In contrast, in this dissertation we focus on learning how and why such content is portrayed. Rather than viewing media for its content, we recast the problem as understanding visual communication and visual rhetoric. For example, the same content may be portrayed in different ways in order to present the story the author wishes to convey. We thus seek to model not only the content of the media, but its authorial intent and latent messaging. Understanding how and why visual content is portrayed a certain way requires understanding higher level abstract semantic concepts which are themselves latent within visual media. By latent, we mean the concept is not readily visually accessible within a single image (e.g. right vs left political bias), in contrast to explicit visual semantic concepts such as objects. Specifically, we study the problems of modeling photographic style (how professional photographers portray their subjects), understanding visual persuasion in image advertisements, modeling political bias in multimedia (image and text) news articles, and learning cross-modal semantic representations. While most past research in vision and natural language processing studies the case where visual content and paired text are highly aligned (as in the case of image captions), we target the case where each modality conveys complementary information to tell a larger story. We particularly focus on the problem of learning cross-modal representations from multimedia exhibiting weak alignment between the image and text modalities. A variety of techniques are presented which improve modeling of multimedia rhetoric in real-world data and enable more robust artificially intelligent systems

A Haptic Modeling System

Haptics has been studied as a means of providing users with natural and immersive haptic sensations in various real, augmented, and virtual environments, but it is still relatively unfamiliar to the general public. One reason is the lack of abundant haptic content in areas familiar to the general public. Even though some modeling tools do exist for creating haptic content, the addition of haptic data to graphic models is still relatively primitive, time consuming, and unintuitive. In order to establish a comprehensive and efficient haptic modeling system, this chapter first defines the haptic modeling processes and its scopes. It then proposes a haptic modeling system that can, based on depth images and image data structure, create and edit haptic content easily and intuitively for virtual object. This system can also efficiently handle non-uniform haptic property per pixel, and can effectively represent diverse haptic properties (stiffness, friction, etc)