Multiple modalities sensor fusion has been widely employed in various surveillance and military applications. A
variety of image fusion techniques including PCA, wavelet, curvelet and HSV has been proposed in recent years
to improve human visual perception for object detection. One of the main challenges for visible and infrared
image fusion is to automatically determine an optimal fusion strategy for different input scenes along with an
acceptable computational cost.
This paper, we propose a fast and adaptive feature selection based image fusion method to obtain high a
contrast image from visible and infrared sensors for targets detection. At first, fuzzy c-means clustering is applied
on the infrared image to highlight possible hotspot regions, which will be considered as potential targets' locations.
After that, the region surrounding the target area is segmented as the background regions. Then image fusion
is locally applied on the selected target and background regions by computing different linear combination of
color components from registered visible and infrared images. After obtaining different fused images, histogram
distributions are computed on these local fusion images as the fusion feature set. The variance ratio which
is based on Linear Discriminative Analysis (LDA) measure is employed to sort the feature set and the most
discriminative one is selected for the whole image fusion. As the feature selection is performed over time, the
process will dynamically determine the most suitable feature for the image fusion in different scenes. Experiment
is conducted on the OSU Color-Thermal database, and TNO Human Factor dataset. The fusion results indicate
that our proposed method achieved a competitive performance compared with other fusion algorithms at a
relatively low computational cost.