图像集合分类主要研究大规模视频序列中涵盖图像复杂表观变化的集合建模表示与分类学习，是当前计算机视觉领域的一个具有重要理论价值和广阔应用前景的研究课题。 本项目将以流形几何和统计学习理论为基础，分析图像集合复杂数据模式的统计分布规律，研究集合数据的紧致特征表示与建模理论，建立集合模型上的距离度量与分类学习方法框架。 具体研究内容包括：针对集合数据的复杂非线性变化，研究类别相关的多流形协同表示学习理论，提出局部概率模型框架下的多流形判别分类方法；分析集合数据分布的统计特性，建立样本统计量表示的集合模型，提出融合多阶统计量特征的集合距离度量学习方法；研究集合分布结构的鲁棒统计优化理论，构建流形与统计量的联合学习模型，解决数据噪声与采样偏差等挑战问题，实现高精度高信度的图像集合分类。 本项目预期取得理论创新与技术突破，促进视频识别的广泛应用。

Image set classification studies the modeling and classification of a set of images, which usually come from large-scale video sequences and cover complex appearance variations. The problem has been a hot topic in the computer vision research community with important theoretical value and broad application prospects. This project will take manifold geometry and statistical learning theory as its foundation, analyze the statistical distribution principle of the complex set data patterns, study the compact feature representation and modeling of image set, and establish a framework of distance metric and classification learning methods for set model. Specifically, this project will address the following research issues. First, to model the complex nonlinear variations of set data, we will study the multi-manifold collaborative learning theory, and propose a multi-manifold discriminant classification approach based on local probabilistic models for manifold representation. Second, we will conduct analysis on the statistical properties of set data distribution, build a sample statistics-based set model, and derive a set distance metric learning method by integrating multi-order statistics features. Third, we will study the robust statistics and optimization theory of set data structure, construct a joint learning model by combining appearance manifold and statistics modeling. With the proposed joint model, we are able to effectively solve the challenging problems of data noise and sampling deviation, and finally achieve appealing image set classification with both high precision and high reliability. This project is devoted to practical applications such as video security surveillance, video retrieval and clustering, web albums management, etc. The project is expected to achieve both theoretical innovation and technological breakthroughs, and finally promote the wide application of video-based object recognition.