Evaluation Methodology for Image Testbed and Content-Based Retrieval

图像测试平台和基于内容的检索的评估方法

• 批准号: 0208936
• 负责人: Aidong Zhang
• 金额: $ 17万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0208936&HistoricalAwards=false
• 关键词: Evaluation; Methodology; Image; Testbed; Content

The project aims at developing an evaluation methodology for content-based image retrieval. The research involves: (1) Measurement of the complexity of image testbeds which can be used to quantitatively determine the degree of difficulty in retrieving images from the image testbeds, and (2) Comparison of the performance of different retrieval approaches which can quantitatively give an objective ranking of the performance of the retrieval approaches. The project designs a general framework of image feature representations which can be used as a vehicle to conduct statistical analysis on images and forms a basis for establishing the evaluation methodology. Using this framework,the evaluation method can measure the complexity of the image databases by their cross entropy and rank the retrieval approaches by their cross entropy with respect to a particular testbed. The image testbeds can thus be compared with each other on their complexity in supporting image querying. Furthermore, the retrieval techniques can be compared with each other without using queries so the human subjectivity is avoided. The algorithms developed in this project will be a valuable asset for the community to eventually establish a general theory of the evaluation methodology for content-based image retrieval research.

该项目旨在开发一种基于内容的图像检索评估方法。研究内容包括：（1）图像测试台复杂度的测量，可以定量确定从图像测试台检索图像的难度；（2）不同检索方法的性能比较，可以定量给出检索方法性能的客观排名。该项目设计了一个图像特征表示的总体框架，可以作为对图像进行统计分析的工具，并为建立评估方法奠定基础。使用该框架，评估方法可以通过图像数据库的交叉熵来衡量图像数据库的复杂性，并根据特定测试平台的交叉熵对检索方法进行排序。因此，可以对图像测试台在支持图像查询方面的复杂性进行比较。此外，检索技术可以在不使用查询的情况下相互比较，因此避免了人为主观性。该项目开发的算法将成为社区的宝贵财富，最终建立基于内容的图像检索研究评估方法的一般理论。