Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
ShadowDraw: real-time user guidance for freehand drawing
ACM SIGGRAPH 2011 papers
From local features to local regions
MM '11 Proceedings of the 19th ACM international conference on Multimedia
On shape and the computability of emotions
Proceedings of the 20th ACM international conference on Multimedia
Towards indexing representative images on the web
Proceedings of the 20th ACM international conference on Multimedia
Image retrieval with query-adaptive hashing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Bundle min-hashing for logo recognition
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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In this paper, we propose Partition min-Hash (PmH), a novel hashing scheme for discovering partial duplicate images from a large database. Unlike the standard min-Hash algorithm that assumes a bag of words image representation, our approach utilizes the fact that duplicate regions among images are often localized. By theoretical analysis, simulation, and empirical study, we show that PmH outperforms standard min-Hash in terms of precision and recall, while being orders of magnitude faster. When combined with the start-of-the-art Geometric min-Hash algorithm, our approach speeds up hashing by 10 times without losing precision or recall. When given a fixed time budget, our method achieves much higher recall than the state-of-the-art.