Computational media aesthetics: finding meaning beautiful

Content management´s future is bright. Innovative media management, annotation, delivery, and navigation services will enrich online shopping, help-desk services, and anytime-anywhere training over wireless devices. Semantics-based annotations will break the traditional linear manner of accessing and browsing media and will support vignette-oriented access of audio and video. This can lead to new offerings of customized media management utilities for various market segments such as online education and training, advertising, news networks, and broadcasting studios. However, the semantic gap between the rich meaning that users want when they query and browse media and the shallowness of the content descriptions that we can actually compute is weakening today´s automatic content-annotation systems. This is a crucial obstacle that we must overcome to achieve that bright future. A serious need exists to develop algorithms and technologies that can annotate content with deep semantics and establish semantic connections between media´s form and function, for the first time letting users access indexed media and navigate content in unforeseeable and surprising ways. To address these underlying problems,, we advocate an approach that markedly departs from existing methods´ based on detecting and annotating low-level audio-visual features. To go beyond representing what a video or movie directly shows, we postulate that we must analyze and interpret the content´s visual, aural, and emotional impact. Our contention is that we must understand compositional and aesthetic media principles to guide content analysis