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INTERACTIVE RETRIEVAL AND FILTERING TECHNIQUES FOR VIDEO DATA

视频数据的交互式检索和过滤技术

基本信息

批准号：
14580424
负责人：
URAHAMA Kiichi
金额：
$ 1.79万
依托单位：
Kyushu University (2004)九州芸術工科大学 (2002-2003)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-14580424/
关键词：
video data similarity search collaborative filtering filtering search latent semantic analysis clustering graph spectral method video analysis 映像検索類似画像検索強調フィルタリング時間コレログラム

项目摘要

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

本研究的目的是视觉媒体的相似性搜索。本文研究了基于相关反馈的交互式检索方法、基于过滤的检索加速技术和基于协同过滤的用户偏好处理方法等个性化检索系统的基本技术。基于图像特征点提取的直方图检索和基于特征向量间双线性形式相似性的图像检索技术，并将这些方法应用于视频帧的相似性检索。在研究了混合分布视频的表示方法后，提出了一种基于混合分布距离的视频检索方法。接下来，我们提出了一个视频的特征向量，称为时间相关性。 ...更多信息表示连续视频帧之间的相关性的时间相关图，并且通过使用该时间相关图，我们已经开发了用于视频的相似性搜索技术。我们还开发了一种基于潜在语义分析的视频相似性搜索方法，通过视频镜头配置列联表来表示视频数据库。为了提高这种基于潜在语义分析的视频检索方法的性能，我们提出了一种鲁棒的聚类方法，并提出了一些过滤搜索技术，如基于特征向量间双线性形式距离下界的过滤，滤波基于时间相关图的上界，基于图直方图下界的过滤和基于直方图交集上下界的过滤搜索技术。我们研究了用于向用户推荐视频的协同过滤方法，他们对视频的预测偏好我们已经开发了一种加速方法的协同过滤聚类用户，一个快速的协同过滤方法的偏好估计的基础上项目之间的相关性，并选择一组最佳的项目呈现给一个新的用户的方法。少