Title :
Nonparametric regression modeling with topographic maps as a basis for lossy image compression
Author :
Van Hulle, Marc M.
Author_Institution :
Lab. voor Neuro- en Psychofysiologie, Katholieke Univ., Leuven
Abstract :
We introduce a new approach to lossy image compression with topographic maps, a type of neural net, based on nonparametric regression modeling: the topographic maps are trained to perform nonparametric regression using the author´s maximum entropy learning rule (1995, 1997), in combination with projection pursuit regression learning. Furthermore, in order to better account for the local image statistics, we apply a technique similar to subspace classification. Finally, we compare the performance of our approach to that of the Karhunen-Loeve transform and the optimally integrated adaptive learning algorithm
Keywords :
data compression; image coding; learning (artificial intelligence); maximum entropy methods; neural nets; nonparametric statistics; statistical analysis; Karhunen-Loeve transform; local image statistics; lossy image compression; maximum entropy learning rule; neural net; nonparametric regression modeling; optimally integrated adaptive learning algorithm; projection pursuit regression learning; subspace classification; topographic maps; Clustering algorithms; Entropy; Image coding; Karhunen-Loeve transforms; Laboratories; Neural networks; Neurons; Partitioning algorithms; Statistical distributions; Vectors;
Conference_Titel :
Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Conference_Location :
Amelia Island, FL
Print_ISBN :
0-7803-4256-9
DOI :
10.1109/NNSP.1997.622378
