CDI-Type I: Multi-modal Interface for Retrieval of Perceptually and Semantically Similar Biomedical Images

使用专家输入的自动标注仍然是一个具有挑战性的问题。团队计划使用视觉感知研究、以用户为中心的设计和基于图像内容的方法的混合方法。应用领域将首先是生物医学图像的分类，这将具有研究和教学以及临床诊断的潜力。来自用户的一组紧密结合的可视数据应同时提供图像分类方案和数据库组织的信息，这些用户还提供用于生成注释的口头数据。该项目有两个主要目标：将视觉感知研究和以用户为中心的设计相结合，进行特征识别和开发合适的元数据；设计和构建一个可用的、多模式的、基于凝视感知的基于内容的图像检索系统，用于多分类图像的交互式搜索和检索。评估将包括专家和新手的检索性能和系统可用性指标。研究生将是这项研究的持续部分。成果将通过期刊和会议传播，数据将在一个共享网站上提供。

Automatic annotation that uses expert input remains a challenging problem. The teams plans to use a mixture of methods from visual perception research, user-centered design, and image-content based methods. The application field will initially be categorization of biomedical images, which have potential for research and teaching, as well as clinical diagnosis. A closely integrated set of visual data, from users who also provide verbal data for production of annotation should information both image classification schemes and database organization. The project has two major aims: to combined visual perception research and user-centered design to do feature identification and develop appropriate metadata; and to design and build a usable, multimodal, gaze-aware content-based image retrieval system for interactive search and retrieval of multiply-classified images. Evaluation will include both retrieval performance and system usability metrics for both experts and novices. Graduate students will be an ongoing part of the research. Outcomes will be disseminated through journals and conferences, and data will be made available on a shared website.