INTERACTIVE RETRIEVAL AND FILTERING TECHNIQUES FOR VIDEO DATA

本研究的目的是对视觉媒体进行相似性搜索。首先提出了基于边缘方向直方图的相似度搜索、基于图像特征点提取的图形直方图检索、基于特征向量之间双线性形式相似度的图像检索等图像检索方法，然后将这些图像检索方法应用于视频帧的相似度搜索。在研究混合分布视频表示方法的基础上，提出了一种基于混合分布距离的视频检索方法。接下来，我们提出了一种称为时间相关…的视频特征向量更多的时间相关图，用来表示连续视频帧之间的相关性，并利用这种时间相关性图，我们提出了一种视频相似性搜索技术。提出了一种基于潜在语义分析的视频相似度搜索方法，用视频镜头构型关联表表示视频数据库。为了提高基于潜在语义分析的视频检索方法的性能，我们提出了一种稳健的聚类方法，并在此基础上提出了基于特征向量双线性形式距离下界的过滤、基于时间相关图上界的过滤、基于图直方图下界的过滤和基于直方图交点上下界的过滤搜索技术，并对基于用户预测视频偏好的协同过滤视频推荐方法进行了研究。我们提出了一种基于用户聚类的协同过滤加速方法，一种基于项目间相关性的快速协同过滤方法，以及一种选择最优项目集呈现给新用户的方法。较少

The aims of this research is the similarity search of visual media. We have developed some basic techniques for personalizing retrieval systems such as interactive retrieval methods based on relevance feedbacks, speeding-up techniques by filtering retrieval methods and user preference handling methods based on collaborative filtering techniques.At first, we have developed some image retrieval methods such as the similarity search by using edge orientation histograms, retrieval with a graph histogram based on extraction of image feature points and an image retrieval technique using bilinear form similarity between the feature vectors.We have next applied these image retrieval methods to the similarity search of video frames. We developed firstly a video retrieval method based on the distance between mixture distributions after the development of the representation method for videos with mixture distributions of frames. We next proposed a feature vector of videos called the temporal corr … More elogram which represents the correlation between consecutive video frames and by using this temporal correlogram we have developed a similarity search technique for videos. We have also developed a similarity search method of videos based on the latent semantic analysis with the representation of the video database by a contingency table of shot configuration of videos. We have developed a robust clustering method for the improvement in the performance of this video retrieval method based on the latent semantic analysis.At the aim of acceleration of the search of images or videos based on these methods, we have developed some filtering search techniques such as the filtering based on the lower bound of the bilinear form distance between feature vectors, the filtering based on the upper bound of the temporal correlogram, the filtering based on the lower bound of the graph histograms and the filtering search technique based on the upper and the lower bounds of the histogram intersection.We have examined the collaborative filtering method for recommendation of videos for users according to their predicted preference of videos. We have developed an acceleration method for the collaborative filtering by clustering users, a fast collaborative filtering method of preference estimation based on the correlation between items, and a method for selecting an optimal set of items for presenting them to a new user. Less