Most Recent Update Ever Up Until Another Update

As mentioned in the previous post, images can seemingly shift towards the bottom right corner when using the standard gaussian kernel in OpenCV in its pyramid function. To get around this, we wrote our own code to do the pyramids and now this issue is far less noticeable. This is a side effect from using the pyramids - they are much faster than applying a wide bank of filters but come with this small downside.

We are experimenting with different ways to weight the various maps we combine for our final saliency map. We tried one method which waited them according to their mean intensity values, but found the results to be very inconsistent.

We're also producing both an RGB based and Lab based set of maps now so we can visually compare them. In most cases Lab seems to outperform the RGB based maps - sometimes to a great degree:

Original Image:

Final Lab Map:

Final RGB Map:

I've also been playing around with how we are representing images. Currently we are using 8 bits per channel so pixel values are integers from 0 to 255. I tried an implementation where all image data was stored as floating point values, 0 to 1, and the results were slightly different. This is something we need to investigate more as we continue.

The normalization technique we are currently using involves multiplying the image by (max - mean(local_max))^2, which has the issue of non-fixed range, which is why our final maps have large regions of similar intensities instead of a more gradual build up. A newer technique proposed by Itti et al. involves iterating a Difference of Guassians filter over the image to perform normalization. We have experimented with this but are having issues getting it to work as expected - our images come out very blurred.

Our immediate plans are to implement a low level symmetry filter and use that in our final saliency map. In addition we are going to refine our current methods as stated above and see if any improvement is detected.