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20 December 2001 Semi-automatic feedback using concurrence between mixture vectors for general databases
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This paper describes how a query system can exploit the basic knowledge by employing semi-automatic relevance feedback to refine queries and runtimes. For general databases, it is often useless to call complex attributes, because we have not sufficient information about images in the database. Moreover, these images can be topologically very different from one to each other and an attribute that is powerful for a database category may be very powerless for the other categories. The idea is to use very simple features, such as color histogram, correlograms, Color Coherence Vectors (CCV), to fill out the signature vector. Then, a number of mixture vectors is prepared depending on the number of very distinctive categories in the database. Knowing that a mixture vector is a vector containing the weight of each attribute that will be used to compute a similarity distance. We post a query in the database using successively all the mixture vectors defined previously. We retain then the N first images for each vector in order to make a mapping using the following information: Is image I present in several mixture vectors results? What is its rank in the results? These informations allow us to switch the system on an unsupervised relevance feedback or user's feedback (supervised feedback).
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Mohamed-Chaker Larabi, Noel Richard, Olivier Colot, and Christine Fernandez-Maloigne "Semi-automatic feedback using concurrence between mixture vectors for general databases", Proc. SPIE 4672, Internet Imaging III, (20 December 2001); doi: 10.1117/12.452690;

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