Title :
On optimal segmentation of sequential data
Author :
Kohlmorgen, Jens
Author_Institution :
Fraunhofer FIRST.IDA, Berlin, Germany
Abstract :
We present an algorithm that efficiently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a by-product, we obtain a regularization parameter that can be used to compute such salient segmentations - also on new data sets - even more efficiently.
Keywords :
image segmentation; image sequences; optimisation; set theory; optimal partitions; optimal segmentation; regularization parameter; salient segmentations; sequential data; Cost function; Dynamic programming; Hidden Markov models; Partitioning algorithms; Robustness; Viterbi algorithm;
Conference_Titel :
Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on
Print_ISBN :
0-7803-8177-7
DOI :
10.1109/NNSP.2003.1318044
