  Visual Saliency is the capability of vision system to select distinctive
parts of scene and reduce the amount of visual data that need to be processed.
The presentpaper introduces (1) a novel approach to detect salient regions by
considering color and luminance based saliency scores using Dynamic Mode
Decomposition (DMD), (2) a new interpretation to use DMD approach in static
image processing. This approach integrates two data analysis methods: (1)
Fourier Transform, (2) Principle Component Analysis.The key idea of our work is
to create a color based saliency map. This is based on the observation
thatsalient part of an image usually have distinct colors compared to the
remaining portion of the image. We have exploited the power of different color
spaces to model the complex and nonlinear behavior of human visual system to
generate a color based saliency map. To further improve the effect of final
saliency map, weutilized luminance information exploiting the fact that human
eye is more sensitive towards brightness than color.The experimental results
shows that our method based on DMD theory is effective in comparison with
previous state-of-art saliency estimation approaches. The approach presented in
this paperis evaluated using ROC curve, F-measure rate, Precision-Recall rate,
AUC score etc.

Computer Vision and Pattern Recognition