Q: Logo detection/recognition in natural images Given a logo image as a reference image, how to detect/recognize it in a cluttered natural image? 
The logo may be quite small in the image, it can appear in clothes, hats, shoes, background wall etc. I have tried SIFT feature for matching without any other preprocessing, and the result is good for cases in which the size of the logo in images is big and the logo is clear. However, it fails for some cases where the scene is quite cluttered and the proportion of the logo size is quite small compared with the whole image. It seems that SIFT feature is sensitive to perspective distortions.  
Anyone know some better features or ideas for logo detection/recognition in natural images? For example, training a classifier to locate candidate regions first, and then apply directly SIFT matching for further recognition. However, training a model needs many data, especially it needs manually annotating logo regions in images, and it needs re-training (needs to collect and annotate new image) if I want to apply it for new logos. 
So, any suggestions for this? Detailed workflow/code/reference will be highly appreciated, thanks!

A: There are many algorithms from shape matching to haar classifiers. The best algorithm very depend on kind of logo. 
If you want to continue with feature registration, i recommend:


*

*For detection of small logos, use tiles. Split whole image to smaller (overlapping) tiles and perform usual detection. It will use "locality" of searched features. 

*Try ASIFT for affine invariant detection. 

*Use many template images for reference feature extraction, with different lightning , different background images (black, white, gray) 
