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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chester, U.A. Ratsaby, J. |
| Copyright Year | 2012 |
| Description | Author affiliation: Electrical and Electronics Engineering, Department, Ariel University Center of Samaria, Ariel 40700 (Chester, U.A.; Ratsaby, J.) |
| Abstract | We introduce an algorithm for measuring the distance between two images based on computing the complexity of two strings of characters that encode the images. Given a pair of images, our algorithm transforms each one into a text-based sequence (strings) of characters. For each string, it computes the LZ-complexity and then uses the string-distance measure of [1] to obtain a distance value between the images. The main advantages of our algorithm are that it is universal, that is, it neither needs nor assumes any spatial or spectral information about the images, it can measure the distance between two images of different sizes, it works for black and white, grayscale and color images, and it can be implemented efficiently on an embedded computer system. We present successful experimental results on clustering images of different sizes into categories based on their similarities as measured by our algorithm. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 543057 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467346825 |
| e-ISBN | 9781467346818 |
| DOI | 10.1109/EEEI.2012.6377115 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-14 |
| Publisher Place | Israel |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Gray-scale Complexity theory Classification algorithms Color History Prototypes Feature extraction |
| Content Type | Text |
| Resource Type | Article |
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