Tracking and data association
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Unsupervised Learning of Object Features from Video Sequences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Level Grouping for Video Shots
International Journal of Computer Vision
A Simple Bayesian Framework for Content-Based Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Semantic-Shift for Unsupervised Object Detection
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Video clip matching using MPEG-7 descriptors and edit distance
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Video retrieval using high level features: exploiting query matching and confidence-based weighting
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
DISCOV: A Framework for Discovering Objects in Video
IEEE Transactions on Multimedia
An effective CBVR system based on motion, quantized color and edge density features
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Video retrieval based on words-of-interest selection
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Words-of-interest selection based on temporal motion coherence for video retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Improving bag-of-visual-words model with spatial-temporal correlation for video retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
Multimedia information retrieval on the social web
Proceedings of the 22nd international conference on World Wide Web companion
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State-of-the-art video retrieval methods use global image statistics to provide low level descriptors or use object recognizers to provide high level features. Using global image statistics can be hindered by lack of explicitly characterizing the object of interest hence prone to retrieving irrelevant results, while using object recognizers can suffer from having to train a large number of object recognizers for different types of objects. We present a novel framework for content-based video retrieval. We use an unsupervised learning method to automatically discover and locate the object of interest in a video clip. This unsupervised learning algorithm alleviates the need for training a large number of object recognizers. Regional image characteristics are extracted from the object of interest to form a set of descriptors for each video. A novel ensemble-based matching algorithm compares the similarity between two videos based on the set of descriptors each video contains. Videos containing large pose, size, and lighting variations are used to validate our approach.