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Class-specific metrics for multidimensional data projection applied to CBIR

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Abstract

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

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Acknowledgements

We thank the anonymous reviewers for their useful and constructive comments. This work was supported by FAPESP and CAPES-Brazil.

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Authors and Affiliations

  1. Instituto de Ciências Matemáticas e de Computação—ICMC, Universidade de São Paulo—USP, São Carlos, SP, Brazil

    Paulo Joia, Erick Gomez-Nieto, João Batista Neto, Wallace Casaca, Glenda Botelho, Afonso Paiva & Luis Gustavo Nonato

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  1. Paulo Joia
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  2. Erick Gomez-Nieto
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  3. João Batista Neto
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  4. Wallace Casaca
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  5. Glenda Botelho
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  7. Luis Gustavo Nonato
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Correspondence to Paulo Joia.

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Joia, P., Gomez-Nieto, E., Batista Neto, J. et al. Class-specific metrics for multidimensional data projection applied to CBIR. Vis Comput 28, 1027–1037 (2012). https://doi.org/10.1007/s00371-012-0730-z

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