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Re: [help-GIFT] Asymmetric Similarity Matrix

From: Henning Müller
Subject: Re: [help-GIFT] Asymmetric Similarity Matrix
Date: Fri, 17 Apr 2009 08:15:36 +0200
User-agent: Thunderbird (Windows/20090302)


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 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 <address@hidden <mailto:address@hidden>> 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.

        On Thu, Apr 16, 2009 at 8:14 AM, Henning Müller
        <mailto:address@hidden>>> 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
           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

           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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