کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
535647 | 870359 | 2013 | 8 صفحه PDF | دانلود رایگان |
• Compressibility of a data x is an approximation of the Kolmogorov complexity K(x)K(x).
• Compressibility can be a new general data’s feature for pattern recognition.
• This possibility is shown through the preliminary implementation of the PRDC-CSOR.
• PRDC-CSOR can build the pattern recognition function using incoming data only.
• Preliminary application to image data gave promising results.
The present paper introduces a new data analyzer, a compression-based self-organizing recognizer, the PRDC-CSOR (Pattern Representation scheme using Data Compression – Compression based Self ORganizing Recognizer), with a preliminary application to image data. The PRDC-CSOR is an extension of the authors’ previously proposed pattern representation scheme using data compression (PRDC). Contrary to the traditional statistical-model-based recognition system methods, the PRDC-CSOR constructs itself using incoming data only. The basic tool, compressibility, is an approximation of the Kolmogorov complexity K(x)K(x) defined in an individual text x as a countermeasure against the Shannon entropy H(X)H(X) defined on an ensemble X. Due to this feature, a highly automatic self-organizing recognition system becomes possible as demonstrated in this paper.
Journal: Pattern Recognition Letters - Volume 34, Issue 14, 15 October 2013, Pages 1569–1576