Ai. Gonzalez et al., Basic competitive neural networks as adaptive mechanisms for non-stationary colour quantisation, NEURAL C AP, 8(4), 1999, pp. 347-367
In this paper we consider the application of two basic Competitive Neural N
etworks (CNN) to the adaptive computation of colour representatives on imag
e sequences that show non-stationary distributions of pixel colours. The te
sted algorithms are the Simple Competitive Learning (SCL) algorithm and the
Frequency-Sensitive Competitive Learning (FSCL) algorithm. Both, SCL and F
CSL are the simplest adaptive methods based, respectively, on minimising th
e distortion and on the search for a uniform quantisation. The aim of this
paper is to study several computational properties of these methods when ap
plied to non-stationary clustering as adaptive vector quantisation algorith
ms. Nonstationary colour quantisation is, therefore, representative of the
more general class of non-stationary clustering problems. We expect our res
ults to be meaningful for other algorithms that involve either the minimisa
tion of the distortion or the search for uniform quantisers. We study exper
imentally the effect of the size of the image sample employed in the one-pa
ss adaptation, their robustness to initial conditions, and the effect of lo
cal versus global scheduling of the learning rate.