Title :
Joint Key-Frame Extraction and Object-Based Video Segmentation
Author :
Song, Xiaomu ; Fan, Guoliang
Author_Institution :
Oklahoma State University, Stillwater, OK
Abstract :
In this paper, we propose a coherent framework for joint key-frame extraction and object-based video segmentation. Conventional key-frame extraction and object segmentation are usually implemented independently and separately due to the fact that they are on different semantic levels. This ignores the inherent relationship between key-frames and objects. The proposed method extracts a small number of key-frames within a shot so that the divergence between video objects in a feature space can be maximized, supporting robust and efficient object segmentation. This method can utilize advantages of both temporal and object-based video segmentations, and be helpful to build a unified framework for content-based analysis and structured video representation. Theoretical analysis and simulation results on both synthetic and real video sequences manifest the efficiency and robustness of the proposed method.
Keywords :
Analytical models; Computational modeling; Data mining; Feature extraction; Humans; Indexing; Object oriented modeling; Object segmentation; Robustness; Video sequences;
Conference_Titel :
Application of Computer Vision, 2005. WACV/MOTIONS '05 Volume 1. Seventh IEEE Workshops on
Conference_Location :
Breckenridge, CO
Print_ISBN :
0-7695-2271-8
DOI :
10.1109/ACVMOT.2005.66