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Image classification using the frequencies of simple features

✍ Scribed by Clark S Lindsey; Michael Strömberg

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
122 KB
Volume
21
Category
Article
ISSN
0167-8655

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✦ Synopsis

We investigate using the frequency of simple features to provide image signatures for input to a classi®er. In an approach inspired by the n-gram technique for text classi®cation, a binary image is scanned with a small window, e.g. 3 ´3 matrix and the occurrences of all possible features patterns within that window are counted. A vector with an element for each possible feature is then created with the element coecients proportional to the frequency of the corresponding features or p-grams, e.g. the vector would have 512 elements for a 3 ´3 window. We tested the method by calculating the p-grams of arti®cially created images of four dierent objects and presenting them to a self-organizing map (SOM). We found this classi®cation scheme successful for this limited image domain. The p-gram encoding scheme provides invariance to translation of the objects within the image and tolerance to scale variations as well.

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