Abstract
Video is exploding as a means of communication and expression, and the resultant archives are massive, disconnected datasets. Thus, scholars' ability to research this crucial aspect of contemporary culture is severely hamstrung by limitations in semantic image retrieval, incomplete metadata, and the lack of a precise understanding of the actual content of any given archive. Our aim in the Large Scale Video Analytics (LSVA) project is to address obstacles in both image-retrieval and research that uses extreme-scale archives of video data that employs a human-machine hybrid process for analyzing moving images. We propose an approach that 1) places more interpretive power in the hands of the human user through novel visualizations of video data, and 2) uses a customized on-demand configuration that enables iterative queries.