A noise robust variant of context extraction for local fusion

Context extraction for local fusion (CELF) is a local approach that combines multiple classifier outputs with the help of feature space information. CELF is based on an objective function that integrates context extraction and decision fusion. Context extraction divides the feature space into homogeneous regions; decision fusion combines multiple classifier outputs in each region or context. Although CELF is a generalization of previous fusion methods, it has some problems and noise sensitivity is one of them. The noise sensitivity problem comes from the fact that there are large number of parameters that should be optimized. Some variants of CELF were propose to mitigate the noise sensitivity problem and they showed better results than CELF. However, they also have their own shortcomings. In this paper, we proposed another variant of CELF to reduce the noise sensitivity and to overcome the shortcomings in the variants of CELF. The proposed method, CELF with regularization (CELF-R), uses regularization, which is a well-known method to reduce noise sensitivity by smoothing solution space. The noise robustness in CELF-R helps it to achieve better results than CELF and its variants. Experiments using landmine data sets also support this.