CBIR with SIFT alike features, discrete- vs. continuous-approach

Question

currently I'm dealing with implementing a CBIR-System for object recognition (object classification in detail) and now since I have some working feature-detectors and -descriptors I try to find the best way for handling these features for the task of content based image retrieval.

As far as I know there are two main trends for this task, the discrete- and the continuous-approach. Where discrete stands for methods like bag-of-visual words and codebooks for building up inverted indices to apply methods referring text-retrieval, and continuous stands for methods like Best Bin First search with k-d trees and nearest neighbor classification.

So one main difference between those both approaches is, one works with an extra representation for features like visual-words and the other one works with the n-D features calculated from the descriptor.

My question is now, is there any comparison between the two method for CBIR which could help me in finding the best approach for my task?

Answer 1

The full answer to this question would be quite complex and long. but generally, a continuous method can give you more accurate results, but it's slower as you can effectively build a search index, and you need to work with large descriptors.

you should consider a combination that uses discrete features (visual words) for initial results, and afterwards filter the result set using continuous methods.