[help-GIFT] gift algorithms - separate normalization and CIDF

[Mika Rummukainen]

Hi there,

I've been wondering for a while now for couple of questions.

Question 1:
How does GIFT perform the weighting of features with different algorithms?

>From articles (Content-based query of image databases,inspirations from text
retrieval: inverted files, frequency-based weights and relevance feedback
and
Content-based query of image databases: inspirations from text retrieval
(Pattern Recognition Letters 21))

I managed to find some information but I assume the equations how scores are
calculated (after relevance feedback) for every image are for CIDF only. 

Now I'd like to know how Separate Normalization performs its weighting. From
"Strategies for positive and negative relevance feedback in image retrieval" I
found a "Separately weighted feedback" - is this how separate normalization is
performaing? 

Question 2:
This makes me think that when GIFT uses separate normalization, it first uses
CIDF algorithm to weight the features and then uses separate normalization to
weight even more the weighted features calculated by CIDF. Am I completely on a
wrong path here?

Question 3: 
How is the similarity of images calculated with both algorithms if there would
be no relevance feedback?

cheers,
Mika