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Re: [help-GIFT] Processing multiple-example-queries
From: |
David Squire |
Subject: |
Re: [help-GIFT] Processing multiple-example-queries |
Date: |
Thu, 31 Jul 2003 15:28:32 +1000 |
User-agent: |
Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.3) Gecko/20030312 |
Sailesh Suvarna wrote:
Hello all,
How does GIFT process multiple-example-queries? Does it combine the features to
form a single composite query?
There is no such thing as a "multiple example query", as the term is
used by some at RMIT. I know that folk there will not agree with me, but
I have read their publications in the area, and what they are describing
is identical to what everyone else knows as relevance feedback
mechanisms, the vast majority of which handle multiple feedback images
(or indeed regions). They are creating confusion by using new
terminology for a concept that has been around for decades.
There are two basic approaches to relevance feedback with multiple
feedback images/regions:
1. Merge features from the feedback images (with appropriate weighting)
so that the query in some sense corresponds to a pseudo-image.
2. Query with each example individually, and then merge results.
GIFT uses the first approach, as is described in the Viper publications.
The approach is based on the decades-old Rocchio scheme. For details,
see, for example:
Henning Müller, Wolfgang Müller, Stéphane Marchand-Maillet, Thierry Pun
and David McG. Squire, Strategies for positive and negative relevance
feedback in image retrieval, In Proceedings of the 15th International
Conference on Pattern Recognition, Barcelona, Spain, September 3-8 2000.
http://www.csse.monash.edu.au/~davids/publications/postscript/2000/MuellerHMuellerWMarchandPunSquire_icpr2000.ps.gz
David McG. Squire, Wolfgang Müller, Henning Müller and Jilali Raki,
Content-based query of image databases, inspirations from text
retrieval: inverted files, frequency-based weights and relevance
feedback, In The 11th Scandinavian Conference on Image Analysis
(SCIA'99), pp. 143-149, Kangerlussuaq, Greenland, June 7-11 1999.
http://www.csse.monash.edu.au/~davids/publications/postscript/1999/SquireMuellerMuellerRaki_scia99.ps.gz
From some preliminary experiments, using single-example queries always gave
better results than using 2 and 3-example queries in terms of Interpolated
Recall-Precsion and using only the Global Colour Histogram feature.
Which is what you would expect, since a merged global colour histogram
is not likely to make a lot of sense unless the feedback images are
*very* similar.
Regards,
David
--
Dr. David McG. Squire, Postgraduate Research Coordinator (Caulfield),
Computer Science and Software Engineering, Monash University, Australia
Monash Provider No. 00008C http://www.csse.monash.edu.au/~davids/