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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chum, O. Matas, J. |
| Copyright Year | 1979 |
| Abstract | We propose a randomized data mining method that finds clusters of spatially overlapping images. The core of the method relies on the min-Hash algorithm for fast detection of pairs of images with spatial overlap, the so-called cluster seeds. The seeds are then used as visual queries to obtain clusters which are formed as transitive closures of sets of partially overlapping images that include the seed. We show that the probability of finding a seed for an image cluster rapidly increases with the size of the cluster. The properties and performance of the algorithm are demonstrated on data sets with 104, 105, and 5 × 106 images. The speed of the method depends on the size of the database and the number of clusters. The first stage of seed generation is close to linear for databases sizes up to approximately 234 ¿ 1010 images. On a single 2.4 GHz PC, the clustering process took only 24 minutes for a standard database of more than 100,000 images, i.e., only 0.014 seconds per image. |
| Sponsorship | IEEE Computer Society |
| Page Count | 7 |
| File Size | 2219441 |
| Starting Page | 371 |
| Ending Page | 377 |
| File Format | |
| ISSN | 01628828 |
| Volume Number | 32 |
| Issue Number | 2 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-02-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Large-scale systems Image databases Spatial databases Visual databases Data mining Image retrieval Clustering algorithms Layout Video sharing Content based retrieval bag of words. minHash image clustering image retrieval |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software |
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