SGER: Exploratory Investigation of Hierarchical Image Classification and Database Indexing

职务名称：SGER：随着高分辨率数码相机越来越普及，高质量的数字图像变得越来越容易获得和有用。在大规模的图像档案中，迫切需要支持更有效的图像检索。在这个小赠款实验研究（SGER）项目中，将开发一种新的框架，图像内容表示的基础上使用显着的对象来表征中层图像语义。对于显著目标检测，自动图像分割将与图像区域分类相结合。此外，分层图像分类将被纳入，使面向概念的图像数据库索引和检索，通过探索不同的语义图像概念之间的潜在关系。分层有限混合模型将提出多层次的语义图像概念建模，图像分类和数据库索引，和一个自适应的期望最大化（EM）算法将被开发的模型选择和参数估计。最后，一个新的框架，统计图像建模将被开发，使部分图像匹配的分类和检索的程序。该研究将产生有用的工具，可以显着影响计算机视觉，图像数据库管理，数字图书馆或博物馆管理，以及图像数据挖掘的广泛应用。该项目将为信息技术的跨学科教育提供良好的环境，弥合计算机视觉、数据库、视觉信息检索和机器学习等传统领域之间的差距，使各级学生受益。项目网页：http://www.cs.uncc.edu/~jfan/project3.html

Title: SGER: Exploratory Investigation of Hierarchical Image Classification and Database IndexingPI: Jianping FanAbstractAs high-resolution digital cameras become more affordable and widespread, high-quality digital images become ever more available and useful. There is an urgent need to support more effective image retrieval over large-scale image archives. In this Small Grants for Experimental Research (SGER) project, a novel framework for image content representation will be developed based on using salient objects to characterize middle-level image semantics. For salient object detection, automatic image segmentation will be integrated with image region classification. Also, hierarchical image classification will be incorporated to enable concept-oriented image database indexing and retrieval through exploring the underlying relationships among different semantic image concepts. Hierarchical finite mixture models will be presented for multi-level semantic image concept modeling, image classification and database indexing, and an adaptive Expectation Maximization (EM) algorithm will be developed for model selection and parameter estimation. Finally, a novel framework for statistical image modeling will be developed to enable partial image matching in the procedures for classification and retrieval. The research will lead to useful tools that could significantly impact computer vision, image database management, digital library or museum management, and image data mining for a wide range of applications. The project will provide a good environment for interdisciplinary education in information technology that bridges the gap between traditional areas of computer vision, databases, visual information retrieval, and machine learning to benefit students of all levels.Project web page: http://www.cs.uncc.edu/~jfan/project3.html