Object and Concept Recognition for Content-Based Image Retrieval

基于内容的图像检索的对象和概念识别

• 批准号: 0097329
• 负责人: Linda Shapiro
• 金额: $ 27万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0097329&HistoricalAwards=false
• 关键词: Object; Concept; Recognition; Content; Based

The goal of this research is to develop the necessary methodology for automated recognition of generic object and concept classes (such as buildings, cars, boats, and trees) in digital images in order to substantially improve the process of content-based image retrieval, which has relied mainly on low-level color and texture features for matching queries to database images. The approach has three major aspects: (1) to design new high-level image features including cluster features that group together lower-level features and relationship features that capture spatial relationships among them; (2) to develop a unified representation that can express a large variety of both low- and high-level features in a form that can be used by learning systems; and (3) to automate the development of recognizers for object and concept classes through the use of a hierarchical, multiple classifier methodology. The resulting techniques are being evaluated on several different large image databases, including commercial databases whose images are grouped into broad classes and a ground-truth database that provides a list of the objects in each image. The results of this work will be a new generic object and concept recognition paradigm that can immediately be applied to automated or semi-automated indexing of large image databases. The methodology will help to bridge the gap between the high-level needs of users of image retrieval systems and the low-level features typically extracted from an image. The generic object class recognition algorithms we develop will begin a new era of object recognition research, leaving the geometric domain and entering the conceptual domain.

本研究的目标是开发自动识别数字图像中的通用对象和概念类（例如建筑物、汽车、船只和树木）的必要方法，以大幅改进基于内容的图像检索过程，该过程主要依赖于低级颜色和纹理特征来将查询与数据库图像进行匹配。该方法具有三个主要方面：（1）设计新的高级图像特征，包括将低级特征组合在一起的聚类特征以及捕获它们之间的空间关系的关系特征； （2）开发一种统一的表示形式，能够以学习系统可以使用的形式表达各种低级和高级特征； (3) 通过使用分层、多分类器方法来自动开发对象和概念类的识别器。由此产生的技术正在几个不同的大型图像数据库上进行评估，包括将图像分为广泛类别的商业数据库和提供每个图像中的对象列表的地面实况数据库。这项工作的结果将是一个新的通用对象和概念识别范例，可以立即应用于大型图像数据库的自动或半自动索引。该方法将有助于弥合图像检索系统用户的高级需求与通常从图像中提取的低级特征之间的差距。我们开发的通用对象类别识别算法将开启对象识别研究的新时代，离开几何领域进入概念领域。