Algorithmic graph theory
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Edge, Junction, and Corner Detection Using Color Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The Earth Mover's Distance under Transformation Sets
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Common Pattern Discovery Using Earth Mover"s Distance and Local Flow Maximization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Region based image annotation through multiple-instance learning
Proceedings of the 13th annual ACM international conference on Multimedia
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
Mining and cropping common objects from images
Proceedings of the international conference on Multimedia
Common visual pattern discovery via graph matching
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Indexing the earth mover's distance using normal distributions
Proceedings of the VLDB Endowment
MIFT: A framework for feature descriptors to be mirror reflection invariant
Image and Vision Computing
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This paper proposes a new approach for the discovery of common patterns in a small set of images by region matching. The issues in feature robustness, matching robustness and noise artifact are addressed to delve into the potential of using regions as the basic matching unit. We novelly employ the many-to-many (M2M) matching strategy, specifically with the Earth Mover's Distance (EMD), to increase resilience towards the structural inconsistency from improper region segmentation. However, the matching pattern of M2M is dispersed and unregulated in nature, leading to the challenges of mining a common pattern while identifying the underlying transformation. To avoid analysis on unregulated matching, we propose localized matching for the collaborative mining of common patterns from multiple images. The patterns are refined iteratively using the expectation-maximization algorithm by taking advantage of the ''crowding'' phenomenon in the EMD flows. Experimental results show that our approach can handle images with significant image noise and background clutter. To pinpoint the potential of Common Pattern Discovery (CPD), we further use image retrieval as an example to show the application of CPD for pattern learning in relevance feedback.