Applying Multiple-Instance Learning to Content-Based Image Retrieval

多示例学习在基于内容的图像检索中的应用随着网络的发展，文本和数字媒体的数量都出现了爆炸式增长。 有效利用这些信息需要有效的技术来查找相关材料。 文本搜索的算法，如关键字搜索的字符串匹配，是很好理解的。 然而，有效的技术来检索imagesbased语义内容需要开发。 由于图像存储库的规模、图像内容的丰富性以及人类感知的主观性，手动关键字标注是不可行的。 本计画将新的分割方法与多范例学习（一种新的机器学习技术）应用于影像搜寻问题。 研究活动承诺，使有效的图像搜索中，用户提供一个所需的图像的例子，并指出哪些少量的候选图像是可取的。 与现有的基于内容的图像检索（CBIR）系统不同，该项目使用多实例学习来自动确定图像的哪些部分对用户来说是重要的。 正如文本搜索技术彻底改变了人们搜索文本信息的方式一样，基于语义内容的高效图像检索有可能在人们处理图像的方式上大幅提高生产力，包括更好地利用越来越多的图像信息，并带来经济效益。 本科生和研究生将通过独立的研究经验，课堂演示和课程最终项目接受培训。 PI将鼓励更多的妇女学习计算机科学，通过与高中妇女的互动，通过华盛顿大学女工程师协会（SWE）的推广活动，并通过与大学妇女的互动，通过SWE的指导活动。 研究成果的传播将通过会议报告和论文、期刊出版物进行。 论文和软件也将在www.cs.wustl.edu/~sg上提供。

Applying Multiple-Instance Learning to Content-Based Image RetrievalWith the development of the web, there has been an explosion in the volume of both textualand digital media. Effective use of that information requires efficient technology forlocating relevant material. Algorithms for textual search, such as string matching forkeyword searches, are well understood. However, effective techniques to retrieve imagesbased on semantic content need to be developed. Manual keyword annotation is not feasibledue to the size of the image repositories, the rich contents of the images, and thesubjectivity of human perception. This project applies new segmentation methods andmultiple-instance learning, a new machine learning technique, to the image searchproblem. The research activities promise to enable effective image search in which a userprovide an example of a desired image and indicates which of a small number of candidateimages are desirable. Unlike existing content-based image retrieval (CBIR) systems, thisproject uses multiple-instance learning to automatically determine which portion(s) of theimage are important to the user. Just as textual search technology has revolutionized theway people search for textual information, efficient image retrieval based on semanticcontent has the potential for large productivity gains in the way people work with images,including better utilization of the increasing volume of information available as images,with accompanying economic benefits. Undergraduate and graduate students will receivetraining through independent research experiences, classroom presentations, and coursefinal projects. The PI will encourage more women to study computer science throughinteractions with high school women through the outreach activities of WashingtonUniversity's Society of Women Engineers (SWE) and through interaction with college womenthrough mentoring activities of SWE. Dissemination of the research results will bethrough conference presentations and papers, journal publications. Also papers andsoftware will made available at www.cs.wustl.edu/~sg.