Comments (4)
This morning I implemented K(d, thetaR, thetaG, thetaB) and it still doesn't recognize yellow highway stripe as salient. I will see if the stripe is salient in any channel taken singly. I am also wondering whether eliminating or lessening the weighting might help. Since as you suggest the weighting lowers the entropy of featureless areas, the yellow stripe and the road it is on are rather uniform.
Re depth-enabled, I haven't easily found such image data.
from saliencydetection.
I did create an RGBD image. It and its components are in my repository at data/RGBD/rgbdfromtumunichstructurenotexture.png etc.
Its a difficult test. It is no surprise that the algorithm does not find many edges in the RGB version, and does find more object edges in the RGBD version, but even the depth data does not let it find the frontmost wall corner.
The name of the file (as given by TechUMunich) indicates the nature of the image: mostly one color and texture with varied depth. The image in this post doesn't do it justice, it seems to have flattened the image taking the fourth channel as transparency.
/
from saliencydetection.
Sorry for the delay, but I will try to respond to all your issues / suggestions.
I'm not sure about the kernel sum of K(d, thetaR, thetaG, thetaB). I've noticed in the past when other authors presented work for color saliency, they used a different color space or just one that was in tune with the human visual system.
from saliencydetection.
You may have to define saliency in a different manner in order to highlight the yellow stripe. The relational distribution based on feature differences may not be ideal in that case.
You are doing interesting work. I will give some thought to your ideas and get back to you.
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