Context extraction for local fusion using fuzzy clustering and feature discrimination

Abstract

We present a novel method for fusing different classifiers outputs. Our approach, called Context Extraction for Local Fusion with Feature Discrimination (CELF-FD), is a local approach that adapts the fusion method to different regions of the feature space. It is based on a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. This objective function is defined and optimized to produce contexts as compact clusters in subspaces of the high-dimensional feature space via unsuper-vised clustering and feature discrimination. Optimization of the objective function also provide optimal fusion parameters for each context. Our initial experiments have indicated that the proposed fusion approach outperforms all individual classifiers and the global fusion method.