Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Semi-Supervised Cross Feature Learning for Semantic Concept Detection in Videos
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Hidden Markov models for automatic annotation and content-based retrieval of images and video
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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Existing techniques to automatically create image thumbnail(s) for videos are mostly based on low-level feature analysis of the video frames, such as color and motion information. However, these approaches do not contain semantic models of the underlying theme of the video, and as a result, the selected frames may not be semantically representative. To address this problem, we propose a theme-based keyframe selection algorithm that explicitly models the visual characteristics of the underlying video theme. This thematic model is constructed by finding the common features of relevant visual samples, which are obtained by querying a visual database with keywords associated with the video. Our initial testing on a set of videos shows promising results of our video thumbnail image selection method.