"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Webified video: media conversion from TV program to web content and their integrated viewing method
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
ACM SIGGRAPH 2005 Papers
ACM SIGGRAPH 2005 Papers
Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain
Journal of Visual Communication and Image Representation
ContextSeer: context search and recommendation at query time for shared consumer photos
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Photo-based question answering
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Boosting object retrieval by estimating pseudo-objects
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Hi-index | 0.00 |
The touch-based displays (devices) have entailed rich interactions between the videos and users. The objects appearing in videos usually interest users in wanting to know relative knowledge about them. In this paper, we proposed a video playback system for users to interactively query objects of interest in videos. Since the text information accompanied with videos might not be strongly related to the object of interest, we adopt visual appearances as features to retrieve similar objects from large image collections. The tags associated with the retrieved images are used to reveal related information of the object of interest for further exploiting related knowledge. Solely relying on single viewpoint of the object to query may suffer from different poses, occlusions and is not robust. So we present a novel video object segmentation approach to improve retrieval precision. The approach is based on a 3D graph cut framework. To ensure prompt response and effectiveness, we augment the algorithm with compressed-domain motion vectors; compared with the prior method, the processing speed of our approach is significantly faster. The experiments on community-contributed videos demonstrate the effectiveness of our approach based on multi-frame object region query and the improvement of retrieval precision.