Introduction to Algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
From frequent itemsets to semantically meaningful visual patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental discovery of object parts in video sequences
Computer Vision and Image Understanding
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
A Hierarchical Visual Model for Video Object Summarization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Common visual pattern discovery via graph matching
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Discovering common objects that appear frequently in a number of images is a challenging problem, due to (1) the appearance variations of the same common object and (2) the enormous computational cost involved in exploring the huge solution space, including the location, scale, and the number of common objects. We characterize each image as a collection of visual primitives and propose a novel bottom-up approach to gradually prune local primitives to recover the whole common object. A multi-layer candidate pruning procedure is designed to accelerate the image data mining process. Our solution provides accurate localization of the common object, thus is able to crop the common objects despite their variations due to scale, view-point, lighting condition changes. Moreover, it can extract common objects even with few number of images. Experiments on challenging image and video datasets validate the effectiveness and efficiency of our method.