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Relevance feedback based on n-tuplewise comparison and the ELECTRE methodology and an application in content-based image retrieval

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Abstract

In this article we propose a method for information retrieval based on relational Multi-Criteria Decision Making. We assume that a user cannot define precise search criteria so that these criteria must be found based on the user’s assessment of several sample alternatives (‘alternatives’ here are database records, e.g. images). This situation is common in Content-based Image Retrieval, where it is easier for a user to indicate relevant images than to describe a proper query, especially in formal language. The proposed algorithm for the elicitation of criteria is based on ELECTRE III—a method originally designed for ranking a set of alternatives according to defined criteria. In our algorithm, however, the direction of reasoning is reversed: we start with several sample alternatives that have been assigned a rank by the user and then we select criteria that are compatible (in the sense of ELECTRE methodology) with the user’s preferences expressed on a sample set. Then, having determined the user’s criteria, we apply classical ELECTRE III to retrieve the relevant solutions from the database. We implemented the method in Matlab and tested it on the Microsoft Cambridge Image Database.

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Notes

  1. The first ELECTRE method (ELECTRE I) was published in [14]

  2. The database is available for non-commercial purposes at:http://research.microsoft.com/en-us/projects/objectclassrecognition/default.htm

  3. In our interface N is fixed but the proposed method can be used with a larger set of sample images and the value of N does not influence significantly the computational complexity of the algorithm.

  4. In CBIR systems with large databases the page zero problem is reduced by applying textual search to obtain the initial set of images: Grigorova, A. and F. D. Natale (2006).

  5. The actual number of criteria found by the algorithm is greater, e.g. when looking for large objects, found criteria are: area, width, height, etc.

  6. We used 1 byte per element (uint8 type, with scaling between [0..1] and [0..255])

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Acknowledgments

This work was supported by the Polish Ministry of Science and Higher Education under SIMPOZ project, no. 0128/R/t00/2010/12. We thank to our colleagues who participated in experiments and to anonymous reviewers for many valuable comments and suggestions.

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  1. AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Krakow, Poland

    Pawel Rotter

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Rotter, P. Relevance feedback based on n-tuplewise comparison and the ELECTRE methodology and an application in content-based image retrieval. Multimed Tools Appl 72, 667–685 (2014). https://doi.org/10.1007/s11042-013-1384-1

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