Digital Pattern Recognition by Moments
Journal of the ACM (JACM)
A New Pattern Representation Scheme Using Data Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Model Conditioned Data Compression Based Similarity Measure
DCC '08 Proceedings of the Data Compression Conference
Dictionary based color image retrieval
Journal of Visual Communication and Image Representation
Algorithmic Cross-Complexity and Relative Complexity
DCC '09 Proceedings of the 2009 Data Compression Conference
On the Complexity of Finite Sequences
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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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) defined in an individual text x as a countermeasure against the Shannon entropy 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.