SBIR Phase I: Automated Image Annotation

SBIR 第一阶段：自动图像注释

• 批准号: 0441570
• 负责人: Ryan Benton
• 金额: $ 10万
• 依托单位: STAR SOFTWARE SYSTEMS CORP
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0441570&HistoricalAwards=false
• 关键词: SBIR; Phase; Automated; Image; Annotation

This Small Business Innovation Research (SBIR) Phase I project examines the feasibility of automatically assigning keywords to new images using instance-based learning methods, content analysis and pre-annotated images. This project will provide a new color quantization method that describes images using a common set of representative and discriminatory colors. This results in a reduction of the number of calculations required by the distance metrics employed by instance-based methods in determining which keywords to assign. This project will also investigate the utility of transforming the low-level color domain into a new feature domain, effectively removing the correlations between color features. This reduces the potential for annotation error when the instance-based methods employ distance metrics that does not account for correlations. Users implicitly refine these initial annotations when they perform searches using relevance feedback. By examining the user feedback, the relative importance of the keywords assigned to an image is modified. The more relevant keywords are assigned greater weights and the less relevant smaller weights. This permits erroneously assigned keywords to be effectively ignored. Hence, this system provides an efficient means for automatically assigning keywords to images and allows for automatic corrections by incorporating the results of user searches.This project will provide organizations that are involved in image generation and/or collection to easily and inexpensively annotate new images. Savings are achieved in both the monetary and efficiency arenas. By automating the annotation process, the need for a staff dedicated to examining and categorizing raw data is greatly reduced, resulting in reduction of costs. Furthermore, by automating the process, the speed at which annotations can be assigned is enhanced, allowing for greater throughput. In addition, by annotating the images, keyword-based search and retrieval systems can now be used on the organization's image collection, allowing for greater leverage of existing software products and permitting greater exposure and utilization of the collection.

这个小型企业创新研究（SBIR）第一阶段项目研究了使用基于实例的学习方法，内容分析和预注释图像自动为新图像分配关键字的可行性。 这个项目将提供一个新的颜色量化方法，描述图像使用一组共同的代表性和歧视性的颜色。 这导致减少了基于实例的方法在确定分配哪些关键字时采用的距离度量所需的计算数量。 本项目还将研究将低级颜色域转换为新特征域的实用性，有效地消除颜色特征之间的相关性。 当基于实例的方法采用不考虑相关性的距离度量时，这减少了注释错误的可能性。 当用户使用相关性反馈执行搜索时，他们隐式地细化这些初始注释。 通过检查用户反馈，修改分配给图像的关键字的相对重要性。 相关性越高的关键字被赋予越大的权重，相关性越低的关键字被赋予越小的权重。这允许错误分配的关键字被有效地忽略。 因此，该系统提供了一种有效的手段，自动分配关键字的图像，并允许通过纳入用户搜索的结果进行自动更正。该项目将提供参与图像生成和/或收集的组织，以方便和廉价的注释新的图像。在资金和效率两个方面都实现了节约。 通过自动化注释过程，对专门检查和分类原始数据的工作人员的需求大大减少，从而降低了成本。 此外，通过自动化该过程，可以提高分配注释的速度，从而允许更大的吞吐量。 此外，通过对图像进行注释，现在可以在本组织的图像集上使用基于关键词的搜索和检索系统，从而可以更好地利用现有软件产品，并使图像集得到更多的展示和利用。