Fast Searching of Multimedia Objects by Content

这个项目的目标是开发一个独立于领域的方法来搜索多媒体数据库的内容，例如，以在视频剪辑集合中找到与新闻广播会话相似的视频剪辑。 该方法从每个对象中提取N个数值特征，有效地将对象映射到N维空间中的一个点。 然后，使用高度微调的现成空间访问方法（如R树）对数据进行聚类，并快速搜索相似对象，即N-d空间中的附近点。 这种方法是通用的，可以用于任何数据库的多媒体对象，只要适当的功能可以得到。 该项目的具体应用是：（a）自然和医学图像，采用最先进的信号处理方法，如小波和形态学，以及（B）语音和相关的时间序列，包括时间扭曲和隐马尔可夫模型。 这种系统的应用很多，包括计算机辅助医学诊断和教学（通过对较旧的专业诊断的医学图像使用相似性搜索），按内容查询视频（在艺术，娱乐或新闻应用中），以及在金融和科学时间序列中的相似性搜索（用于数据挖掘，决策支持和预测）。

The goal of this project is to develop a domain-independent method to search multimedia databases by content, e.g., to find video clips that are similar to a news broadcasting session in a collection of video clips. The approach extracts N numerical features from each object, effectively mapping the object into a point in N-dimensional space. Then, highly fine-tuned, off-the-shelf, spatial access methods (like R-trees) are used to cluster the data and to search very quickly for similar objects, that is, nearby points in the N-d space. This approach is generic and can be used for any database of multimedia objects, as long as appropriate features can be derived. The specific applications in this project are: (a) natural and medical images, with state-of-the-art signal processing methods, like wavelets and morphology, and (b) voice and related time sequences that include time-warping and Hidden Markov Models (HMM). Applications of such a system are numerous, including computer aided medical diagnosis and teaching (by using similarity search on older, expertly diagnosed medical images), queries on video by content (in art, entertainment, or journalism applications), and similarity searching in financial and scientific time sequences (for data mining, decision support, and forecasting).