Video retrieval of near-duplicates using κ-nearest neighbor retrieval of spatio-temporal descriptors

This paper describes a novel methodology for implementing video search functions such as retrieval of near-duplicate videos and recognition of actions in surveillance video. Videos are divided into half-second clips whose stacked frames produce 3D space-time volumes of pixels. Pixel regions with consistent color and motion properties are extracted from these 3D volumes by a threshold-free hierarchical space-time segmentation technique. Each region is then described by a high-dimensional point whose components represent the position, orientation and, when possible, color of the region. In the indexing phase for a video database, these points are assigned labels that specify their video clip of origin. All the labeled points for all the clips are stored into a single binary tree for efficient -nearest neighbor retrieval. The retrieval phase uses video segments as queries. Half-second clips of these queries are again segmented by space-time segmentation to produce sets of points, and for each point the labels of its nearest neighbors are retrieved. The labels that receive the largest numbers of votes correspond to the database clips that are the most similar to the query video segment. We illustrate this approach for video indexing and retrieval and for action recognition. First, we describe retrieval experiments for dynamic logos, and for video queries that differ from the indexed broadcasts by the addition of large overlays. Then we describe experiments in which office actions (such as pulling and closing drawers, taking and storing items, picking up and putting down a phone) are recognized. Color information is ignored to insure independence of action recognition to people's appearance. One of the distinct advantages of using this approach for action recognition is that there is no need for detection or recognition of body parts.     

DAG-based visual interfaces for navigation in indexed video content

Indexing and segmenting of video content by motion, color and texture has been intensively explored leading to a commonly used representation in a storyboard. In this paper, a novel method of visualization of video content is proposed. First of all, the content is segmented into shots, and then a spatio-temporal color signature of shots, based on color distribution in the frames, is proposed. This spatio-temporal color signature serves as a basis for graph clustering and graph visualization tools. Those, integrated in a platform for visualization of huge graphs, Tulip, supply an exciting graph-based navigation interface for multimedia content. The results obtained on feature documentaries are promising.

基于学习对象的数字化学习资源库的开发

学习对象已成为国际上流行的学习资源组织形式。使用元数据描述的数字化学习资源可方便实现管理和复用,提高资源管理的效率。本研究重点讨论学习对象的概念及其显著特征,以及设计策略,如聚合度和组合问题,最后探讨资源库开发过程中涉及到的几个问题,如技术上的具体实现方式,并给出层次结构模型。     

基于傅立叶变换的多维数据可视化分类

基于雷达图表示多维数据的原理,提出一种利用傅立叶描述子识别雷达图形的可视化数据分类新方法。该方法采用多元统计中的雷达图表示多维数据,不同模式类别的多维数据构成不同形状的雷达图形。在此基础上对雷达图的边界曲线进行傅立叶变换,计算傅立叶描述子作为雷达图的图形特征,并运用改进的概率神经网络进行识别。实验结果表明该方法具有较好的分类效果,分类精度比传统分类方法提高了约8.25%。     

Shape ellipticity based on the first Hu moment invariant

In this paper we use the first Hu moment invariant to define a new ellipticity measure. The new ellipticity measure ranges over the interval $(0,1]$ and picks the value 1 if and only if the measured shape is an ellipse. The measure is invariant with respect to translation, rotation and scaling transformations. It is straightforward and fast to compute.