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29 April 2008A generic framework for context-dependent fusion with application to landmine detection
We present a novel method for fusing the results of multiple landmine detection algorithms that use different
types of features and different classification methods. Our approach, called Context Extraction for Local Fusion
(CELF), is motivated by the fact that the relative performance of different detectors can vary significantly
depending on the mine type, geographical site, soil and weather conditions, and burial depth. CELF 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 via unsupervised clustering
while simultaneously providing optimal fusion parameters for each context. Results on large and diverse Ground
Penetrating Radar data collections show that the proposed method can identify meaningful and coherent contexts
and that different expert algorithms can be identified for the different contexts. Typically, the contexts correspond
to groups of alarm signatures that share common attributes such as mine type, geographical site, soil and weather
conditions. Our initial experiments have also indicated that the proposed context-dependent fusion outperforms
all individual detectors and other standard fusion methods.
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Hichem Frigui, Paul D. Gader, Ahmed Chamseddine Ben Abdallah, "A generic framework for context-dependent fusion with application to landmine detection," Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 69531F (29 April 2008); https://doi.org/10.1117/12.782418