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| Content Provider | Springer Nature Link |
|---|---|
| Author | Huang, Jing Ravi Kumar, S. Mitra, Mandar Zhu, Wei Jing Zabih, Ramin |
| Copyright Year | 1999 |
| Abstract | We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors and when computed efficiently, turns out to be both effective and inexpensive for content-based image retrieval. The correlogram is robust in tolerating large changes in appearance and shape caused by changes in viewing position, camera zoom, etc. Experimental evidence shows that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval. We also provide a technique to cut down the storage requirement of the correlogram so that it is the same as that of histograms, with only negligible performance penalty compared to the original correlogram.We also suggest the use of color correlogram as a generic indexing tool to tackle various problems arising from image retrieval and video browsing. We adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. Experimental results again suggest that the color correlogram is more effective than the histogram for these applications, with insignificant additional storage or processing cost. |
| Starting Page | 245 |
| Ending Page | 268 |
| Page Count | 24 |
| File Format | |
| ISSN | 09205691 |
| Journal | International Journal of Computer Vision |
| Volume Number | 35 |
| Issue Number | 3 |
| e-ISSN | 15731405 |
| Language | English |
| Publisher | Kluwer Academic Publishers |
| Publisher Date | 1999-01-01 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing Automation and Robotics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Vision and Pattern Recognition Software |
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