SGER: Exploratory Investigation of Hierarchical Image Classification and Database Indexing

标题：SGER：图像分级分类和数据库索引的探索性研究PI：建平范建平摘要随着高分辨率数码相机变得更加实惠和普及，高质量的数字图像变得更加可用和有用。迫切需要在大规模的图像档案上支持更有效的图像检索。在这个小型实验研究拨款(SGER)项目中，将开发一个新的图像内容表示框架，该框架基于使用显著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，还将结合分层图像分类，通过探索不同语义图像概念之间的潜在关系，实现面向概念的图像数据库索引和检索。提出了用于多层次语义图像概念建模、图像分类和数据库索引的分层有限混合模型，并提出了一种自适应期望最大化(EM)算法用于模型选择和参数估计。最后，将开发一个新的统计图像建模框架，以便能够在分类和检索过程中进行部分图像匹配。这项研究将产生有用的工具，可能对计算机视觉、图像数据库管理、数字图书馆或博物馆管理以及广泛应用的图像数据挖掘产生重大影响。该项目将为信息技术领域的跨学科教育提供一个良好的环境，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，使所有级别的学生受益。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