A new feature fusion method at decision level and its application

Abstract

To solve the problem that using only one feature is poor for the terrestrial environment classification, a new feature fusion method at decision level is proposed in this paper. An eigenvector is obtained by using a Gaussian mixture model (GMM) firstly, and the probabilities of the eigenvector belonging to a certain class of the texture and color are computed. Then the probabilities are multiplied by different weights according to the contribution, and summed to get the maximal likelihood probability to achieve feature fusion. Experimental results demonstrate that the method in this paper is better than other current methods and the classification performance is superior to a single feature obviously.

Keywords

Texture Feature / Discrete Cosine Transform / Gaussian Mixture Model / Color Feature / Fusion Method

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This work has been supported by the National Natural Science Foundation of China (Nos.60705020 and 90820306).