Hi,
this is true for the simple tf/idf weighting that is usually used in
GIFT but it will not work out for all weightings as some of them take
into account the term frequency in the query itself and not only in the
documents and in this case it will not be symmetric. If you use separate
normalization this will not work either as you have to normalize for it.
The separate normalisation has much better results as otherwise small
scale textures can become dominant.
Cheers, Henning
Juan C. Caicedo a écrit :
We were working on analysing both, papers and code. We wonder if the
similarity s(x,y) between two images x and y should be the same as
s(y,x). And of course it is. We found that the asymmetry is a
normalisation effect, in which the results list is scaled by the score
of the query applied on itself. So, when we obtained the scores
normalised using the auto-score of the image x, they are slightly
different to the scores normalised using the auto-score of the image y.
We just modify the source file CQInvertedFile.cc to avoid this
normalization, and now we obtain a symmetric similarity matrix.
Thank you very much for your response.
Juan C. Caicedo
On Thu, Apr 16, 2009 at 4:48 AM, Henning Müller
<[email protected] <mailto:[email protected]>> wrote:
Indeed, the paper:
Tversky, A. (1977). Features of similarity. Psychological Review,
84(4), 327-352.
shows through epxeriments that our visual similarity percetion does
not at all correspond to a metric.
Cheers, Henning
Wolfgang Müller a écrit :
I think the Squire et al. papers from 1999 accessible from the
Viper site in Geneva cite a paper of Tversky's which justifies
asymmetric similarity matrices: What you are looking for
influences your notion of similarity.
Cheers,
Wolfgang
On Thu, Apr 16, 2009 at 8:14 AM, Henning Müller
<[email protected]
<mailto:[email protected]>
<mailto:[email protected]
<mailto:[email protected]>>> wrote:
Dear Juan,
this is normal as the similarity measure of GIFT is not a
metric as
it is based on a tf/idf weighting form text retrieval.
Image similarity is calculated in the space spanned by the
features
present in the query, only. Each image has around 1500
features out
of 87000 possible (most of them binary features), so each image
potentially spans a different sub-space in which similarity is
calculated.
Cheers, Henning
Juan C. Caicedo a écrit :
Hello everybody,
We are building a similarity matrix of an image
collection using
GIFT. However, we notice that this matrix is not a
symmetric one.
Could anybody tells us what is the reason of this
behaviour and
some hints to obtain a symmetric similarity matrix?
Thanks in advance to all you.
Juan C. Caicedo
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