Title :
A unification of relaxation labeling and associative memory
Author :
Zhuang, Xinhua ; Zhao, Yunxin
Author_Institution :
Dept. of Comput. Eng. & Comput. Sci., Missouri Univ., Columbia, MO, USA
Abstract :
This paper attempts to consolidate the theoretical foundation of the relaxation labeling processes, explore the connections between the relaxation labeling model and the Hopfield associative memory model, and seek their unification. We start by defining a new labeling assignment space and then formulate the relaxation labeling process as a dynamic system of Lyapunov type, which is equipped with a well-defined energy function and described by a naturally fitted updating rule. We present a consistency condition and show that each ω-limit point of the dynamic system gives a consistent labeling. We finally make a peace between the multi-label and one-label relaxation labeling and reveal an interesting result that, for a one-label case, the newly formulated relaxation labeling model reduces to the Hopfield associative memory model
Keywords :
Hopfield neural nets; Lyapunov methods; content-addressable storage; relaxation theory; ω-limit point; Hopfield associative memory model; Lyapunov dynamic system; consistency condition; energy function; labeling assignment; multi-label relaxation labeling; one-label relaxation labeling; relaxation labeling; updating rule; Associative memory; Computer errors; Computer networks; Distributed computing; Distributed processing; Error correction; Labeling; Machine vision; Neurons; Power system modeling;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3192-3
DOI :
10.1109/ICASSP.1996.550609