I think the idea of a program that can recognize high level features in images is an excellent idea. The authors' example of the third level feature map of a tiger's stripes versus the third level feature map of a waterfall was also pretty convincing that the authors were headed in the right direction. However, I cannot tell whether this idea generalizes well to all classes of images.

I was a little perplexed by the small discussion of how this technique differs from (and is better than) wavelet filters in terms of phase insensitivity, as I have seen that combining a sine and cosine gabor wavelet filter has the same result.

I agree with the other posts that this paper lacked supporting results. While I'm not familiar with how well other image retrieval systems have performed, and while this system seemed to perform well on at least one type of image (mountains), I feel this system performed fairly poorly on a few classes of images (sunsets and fields), in which it seemed to get things right about 30% of the time, which is not all that much better than guessing.