Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
A robust audio classification and segmentation method
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Single-View Metrology: Algorithms and Applications
Proceedings of the 24th DAGM Symposium on Pattern Recognition
An Assessment of Information Criteria for Motion Model Selection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
Multimodal Video Indexing: A Review of the State-of-the-art
Multimedia Tools and Applications
ACM SIGGRAPH 2005 Papers
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Video motion representation for improved content access
IEEE Transactions on Consumer Electronics
IEEE Transactions on Circuits and Systems for Video Technology
Make3D: depth perception from a single still image
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A nonparametric learning approach to range sensing from omnidirectional vision
Robotics and Autonomous Systems
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In recent years, many features have been suggested to facilitate the task of video retrieval. However, up to now, three-dimensional (3D) scene information has not been utilized to retrieve shots from a database consisting of monocular video sequences. In this paper, we propose the use of depth features for video retrieval purposes. Our depth feature extraction approach is based on a method that originally has been suggested to scan 3D objects with a single camera exploiting the motion parallax. To increase the number of video shots for which the depth feature extraction method is applicable, we present an extension of the self-calibration algorithm of this method. Furthermore, a depth map representation is presented and an adequate distance measure is suggested to compare depth maps. Finally, the extracted depth features are used to retrieve video shots according to the three-dimensional scene content of a shot. Experimental results for the comprehensive TRECVID 2005 video data set demonstrate the usefulness of the proposed depth features for video retrieval.