Tracking and data association
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Semi-Supervised Self-Training of Object Detection Models
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Level Grouping for Video Shots
International Journal of Computer Vision
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Automatic Detection of Object of Interest and Tracking in Active Video
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Automatic Detection of Object of Interest and Tracking in Active Video
Journal of Signal Processing Systems
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We propose a novel method for removing irrelevant frames from a video given user-provided frame-level labeling for a very small number of frames. We first hypothesize a number of candidate areas which possibly contain the object of interest, and then figure out which area(s) truly contain the object of interest. Our method enjoys several favorable properties. First, compared to approaches where a single descriptor is used to describe a whole frame, each area's feature descriptor has the chance of genuinely describing the object of interest, hence it is less affected by background clutter. Second, by considering the temporal continuity of a video instead of treating the frames as independent, we can hypothesize the location of the candidate areas more accurately. Third, by infusing prior knowledge into the topic-motion model, we can precisely follow the trajectory of the object of interest. This allows us to largely reduce the number of candidate areas and hence reduce the chance of overfitting the data during learning. We demonstrate the effectiveness of the method by comparing it to several other semi-supervised learning approaches on challenging video clips.