SBIR Phase I: Automatically Generating Domain Specific Structured Ontologies for Video

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是，通过将目前手工完成的视频注释任务自动化，以一种经济有效的方式使视频可搜索和发现。视频正在以越来越高的速度被创造出来，媒体公司正被其库中庞大的视频数量所淹没。公司已经采取付费方式，让人们手动观看他们的视频，并用相关描述“标记”这些视频，以便内容变得可搜索，从而可用。在这个项目中，该公司将建立一个推理引擎，可以利用视频中的数据和结构来发现对特定领域有意义的特定多模态概念，并自动训练和改进分类器，以应用描述视频的独特和有意义的数据。利用这些元数据，公司可以更有效地索引和搜索他们的视频，使他们能够生成定制的视频片段，以满足特定的目标，并最终以更大的规模发布视频。该公司认为，由其系统生成的独特的特定领域视频元数据将对公司传播信息视频的能力产生压倒性的影响，并改善他们在线视频的使用。这个小企业创新研究（SBIR）第一阶段项目提出开发一个推理引擎，结合视频信息的模式，自动发现和训练特定领域内重要概念的多模式分类器。目前的视频理解方法寻求开发通用的视觉分类模型；这些方法侧重于利用标记数据来训练监督学习算法，以描述视频。这些方法在特定的垂直领域失败，因为信息输出不够细粒度，无法在特定领域的上下文中提供价值，而且手工注释的成本很高。本项目中提出的方法考虑了与一组相关视频相关的多模态信息，以自动学习该视频领域中重要概念的分类器，而无需昂贵的手动注释。该方法利用视频中不同模态与现有领域特定结构之间的相关性来智能地完成此任务。该公司希望这项研究能够发现许多新的特定领域的视频概念分类器，这些分类器在当前的视觉本体中不存在，并提供一种可重用的方法来训练可应用于许多领域的视觉分类器。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase 1 project is to make video searchable and discoverable in a cost-effective manner by automating video annotation tasks that are currently done manually. Video is being created at an increasingly high rate, and media companies are becoming overwhelmed by the sheer amount of video in their libraries. Companies have resorted to paying people to manually watch their videos and "tag" them with relevant descriptions so that the content becomes searchable and therefore useable. In this project the company will build an inference engine that can leverage the data and structure within video to discover specific multimodal concepts significant to the particular domain and automatically train and refine classifiers to apply unique and meaningful data describing the video. Leveraging this metadata, companies can index and search their videos more efficiently, enabling them to generate tailored video clips to meet specific goals, and ultimately publish video at a much larger scale. The company believes the unique domain-specific video metadata generated by its system will have an overwhelming effect on the ability of companies to disseminate informative videos, and improve their use of video online.This Small Business Innovation Research (SBIR) Phase 1 project proposes to develop an inference engine that combines the modalities of information in video to automatically discover and train multimodal classifiers for important concepts within specific domains. Current approaches to video understanding seek to develop general visual classification models; these approaches focus on leveraging labeled data to train supervised learning algorithms in order to describe the video. These approaches fail in specific verticals, because the information output is not granular enough to provide value within the context of the specific domain, and the cost for manual annotation is high. The approach proposed in this project takes into account the multimodal information associated with a group of related videos to automatically learn classifiers for concepts that are important in this video domain, without expensive manual annotation. The approach leverages the correlations between the different modalities in video and existing domain-specific structure to intelligently accomplish this task. The company expects this research to lead to the discovery of many new domain-specific video concept classifiers that do not exist in current visual ontologies, and a reusable approach for training visual classifiers that can be applied across many domains.