Alternative solutions for ASIFT and SIFT?

Question

I'm currently working on comparing objects in different angles for image detection. Basically, I want to know whether the object from image 1 is similar with object from image 2 (% of similarity would be great).

Image1:

Image2:

I have already looked around on the Internet and it seems like ASIFT (LINK) is a great solution. However, when I implement their demo and rerun the demo multiple times with the same inputs, ASIFT gives out different results on matched vertices.

Why does ASIFT give out different results each time I rerun the demo with the same inputs?

PS:
Some comments regarding alternative solutions like ASIFT or SIFT for comparing objects in a different angle (having a more consistent result) would be appreciated as well.

Answer 1

It is not ASIFT or better-ASIFT problem. Basically, ASIFT solves "Wide baseline stereo" problem - find correspondences and geometrical transformations between different views of the SAME object or scene.

What you looking for is some kind of image (object) similarity. State-of-art method for this - train neural net, get fixed length descriptor of image from it and compare descriptors with Eucledian distance between them

For example, have a look into "Neural Codes for Image Retrieval" paper - http://arxiv.org/abs/1404.1777

P.S. If you still need correspondences and gave us different glasses by mistake, you can try MODS http://cmp.felk.cvut.cz/wbs/index.html Difference from ASIFT that it could handle much bigger angluar differences, more stable and much faster.

Answer 2

You can try SURF, which is already implemented in OpenCV.

You may want also to have a look at vlFeat, which is in C, and have Matlab bindings.

Answer 3

This is a quite hard problem for SIFT/ASIFT feature comparison if you have those two images only. It's not that clear even to me to say both images depict the same glasses, having in mind that there are very similar glasses that can be different in, say, the width of the side piece.

That said, I'd look for a different approach. These are some high level approaches that come to my mind:

• The color is very characteristic in this case. If you have two exact models of different color, you can easily say they are not the same. So you can get the color histogram (ignoring the background) and compare them.
• Another very characteristic feature of glasses is the shape of the frame around the lens. According to your images, I'd expect that frame to be visible always. So, you may find the rectangle that encompasses the lens, find the homography between the two images, warp the rectangle and compare both by cross correlation, for example.