Compression of two-dimensional data
IEEE Transactions on Information Theory
Text compression
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
On-line string matching with feedback
Theoretical Computer Science
Selected papers from the 9th Scandinavian conference on Image analysis : theory and applications of image analysis II: theory and applications of image analysis II
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
Linear Algorithm for Data Compression via String Matching
Journal of the ACM (JACM)
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
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A long term goal of research in artificial intelligence is todetermine and to implement principles which permit a movable machine to direct its actions depending uponsensory feed-back from its environment. This paper concentrates onspatial sensors which input images (2-dimensional arrays). A proposal is put forward in which the machine adaptsto the actual data, and examplesare given of input prediction, of detection of unexpected events, andof recognition of spatial patterns.The image sequence is locally partitioned into temporally contiguous subsequences of afixed spatial extent. The spatial extent is constant over time and the temporal extent of a subsequence is maximizedsubject to the condition that the subsequence has occurred previously. The principle is illustrated on image sequences. It is further demonstrated on images which are structured as pseudo-temporal sequences of their rows. The demonstrations use diverse complex and simple examples to illustrate theversatility of the method. The demonstrations show that to a large degree it is not necessary for the user to supply explicit models for different patternrecognition tasks.