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An Integrated Bayesian Approach to Layer Extraction from Image Sequences
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Multiple view geometry in computer visiond
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Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
An Affine Invariant Interest Point Detector
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A Framework for Robust Subspace Learning
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Video Scene Segmentation via Continuous Video Coherence
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Multi-View Subspace Constraints on Homographies
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Video Google: A Text Retrieval Approach to Object Matching in Videos
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Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Machine Vision and Applications
3d object modeling and recognition in photographs and video
3d object modeling and recognition in photographs and video
Integrating multiple model views for object recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Segmenting, modeling, and matching video clips containing multiple moving objects
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Wide-baseline multiple-view correspondences
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Content based video matching using spatiotemporal volumes
Computer Vision and Image Understanding
Feasibility of Personalized Affective Video Summaries
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Proceedings of the 21st annual ACM symposium on User interface software and technology
VideoCut: Removing Irrelevant Frames by Discovering the Object of Interest
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Numismatic Object Identification Using Fusion of Shape and Local Descriptors
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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Computer Vision and Image Understanding
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
Foundations and Trends in Information Retrieval
A unified framework for object retrieval and mining
IEEE Transactions on Circuits and Systems for Video Technology
Places clustering of full-length film key-framesusing latent aspect modeling over SIFT matches
IEEE Transactions on Circuits and Systems for Video Technology
Compositional object recognition, segmentation, and tracking in video
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
A statistical image retrieval method using color invariant
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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Semi-supervised learning of facial attributes in video
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Frontiers of Computer Science: Selected Publications from Chinese Universities
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ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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We describe a method for automatically obtaining object representations suitable for retrieval from generic video shots. The object representation consists of an association of frame regions. These regions provide exemplars of the object's possible visual appearances.Two ideas are developed: (i) associating regions within a single shot to represent a deforming object; (ii) associating regions from the multiple visual aspects of a 3D object, thereby implicitly representing 3D structure. For the association we exploit temporal continuity (tracking) and wide baseline matching of affine covariant regions.In the implementation there are three areas of novelty: First, we describe a method to repair short gaps in tracks. Second, we show how to join tracks across occlusions (where many tracks terminate simultaneously). Third, we develop an affine factorization method that copes with motion degeneracy.We obtain tracks that last throughout the shot, without requiring a 3D reconstruction. The factorization method is used to associate tracks into object-level groups, with common motion. The outcome is that separate parts of an object that are not simultaneously visible (such as the front and back of a car, or the front and side of a face) are associated together. In turn this enables object-level matching and recognition throughout a video.We illustrate the method on the feature film "Groundhog Day." Examples are given for the retrieval of deforming objects (heads, walking people) and rigid objects (vehicles, locations).