CAREER: Machine Learning Based Intelligent Image Annotation and Retrieval

这个职业项目的目标是开发一个跨学科的研究和教育计划，研究基于机器学习的图像标注和检索的基本理论和计算原理。新的研究方法使用基于学习的自动图像标注方法，该方法使用3D-隐马尔可夫模型(HDD)随机模型，该模型直接使用像素级数据，而不是使用分割算法的结果。利用低层特征学习高层语义是实现图像自动标注的重要一步。本项目的研究主要集中在三个方面：(1)开发高效的图像数据机器学习机制；(2)开发高维图像模型；(3)开发基于学习的图像标注和检索系统。本研究将从根本上改进图像标注技术。这一进展将为管理和解释图像数据的问题提供理论上的理解。这项研究的结果也将对图像数据库产生模式识别以外的影响。这些结果中的许多也可以应用于其他机器学习和数据挖掘问题。这项研究将对图像理解和注释的原理有更深入的了解，并最终将有助于创建智能和健壮的多媒体信息系统。教育计划包括开发跨学科课程，促进学生参与该项目的多样性。这项研究的科学成果将增强识别物体和场景的计算机技术，直接应用于在线信息管理、国土安全、军事和包括医疗保健在内的许多科学应用。科学出版物和项目网站http://riemann.ist.psu.edu将用于传播研究成果。

The goal of this CAREER project is to develop an interdisciplinary research and education program for investigating the underlying theoretical and computational principles of machine-learning-based image annotation and retrieval. The novel research approach uses an automated learning-based image annotation method using a 3D-Hidden Markov Model (HDD) stochastic model that directly uses pixel level data as opposed to using the results of a segmentation algorithm. Using low-level features to learn high-level semantics is an important step towards automatic annotation of images. The research of this project focuses on three areas: (1) developing highly efficient machine learning mechanisms for imagery data, (2) developing models for high-dimensional imagery, and (3) developing a learning-based image annotation and retrieval system. This research will fundamentally improve image annotation technologies. This advance will provide theoretical understanding to the problem of managing and interpreting imagery data. The results of this research will also have impacts beyond pattern recognition for image databases. Many of these results can also be applied to other machine learning and data mining problems. This research will gain deeper insights into the principles of image understanding and annotation, and will ultimately contribute to the creation of intelligent and robust multimedia information systems. The educational plan includes developing interdisciplinary curriculum, and promoting diversity in the students' participation in this project. The scientific results of the research will enhance computer technology for recognizing objects and scenes with direct applications in online information management, homeland security, the military, and many scientific applications, including healthcare. Scientific publications and the project Web site http://riemann.ist.psu.edu will be used for the research results dissemination.