Publications

The rate at which video is being created and gathered is rapidly accelerating as access to means of production and distribution expand. This rate of increase, however, is greatly outpacing the development of content-based tools to help users sift through this unstructured, multimedia data. The need for such technologies becomes more acute when considering their potential value in critical, media-rich government applications such as Seized Media Analysis, Social Media Forensics, and Foreign Media Monitoring. A fundamental challenge in developing technologies in these application areas is that they are typically in low-resource data domains. Low-resource domains are ones where the lack of ground-truth labels and statistical support prevent the direct application of traditional machine learning approaches. To help bridge this capability gap, the Joint Audio and Visual Mining of Uncooperatively Collected Video ICE Line Program (2236-1301) is developing new technologies for better content-based search, summarization, and browsing of large collections of unstructured, uncooperatively collected multimedia. In particular, this effort seeks to improve capabilities in video understanding by jointly exploiting time aligned audio, visual, and text information, an approach which has been underutilized in both the academic and commercial communities. Exploiting subtle connections between and across multiple modalities in low-resource multimedia data helps enable deeper video understanding, and in some cases provides new capability where it previously didn't exist. This report outlines work done in Fiscal Year 2014 (FY14) by the cross-divisional, interdisciplinary team tasked to meet these objectives. In the following sections, we highlight technologies developed in FY14 to support efficient Query-by-Example, Attribute, Keyword Search and Cross-Media Exploration and Summarization. Additionally, we preview work proposed for Fiscal Year 2015 as well as summarize our external sponsor interactions and publications/presentations.