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Shape annotation for intelligent image retrieval

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Abstract

Annotation of shapes is an important process for semantic image retrieval. In this paper, we present a shape annotation framework that enables intelligent image retrieval by exploiting in a unified manner domain knowledge and perceptual description of shapes. A semi-supervised fuzzy clustering process is used to derive domain knowledge in terms of linguistic concepts referring to the semantic categories of shapes. For each category we derive a prototype that is a visual template for the category. A novel visual ontology is proposed to provide a description of prototypes and their salient parts. To describe parts of prototypes the visual ontology includes perceptual attributes that are defined by mimicking the analogy mechanism adopted by humans to describe the appearance of objects. The effectiveness of the developed framework as a facility for intelligent image retrieval is shown through results on a case study in the domain of fish shapes.

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Notes

  1. http://www.all-birds.com/Identify.htm

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Acknowledgments

Funding for this work was provided by the Fondazione Cassa di Risparmio di Puglia (FCRP), that supported the Italian project “Annotazione di forme per la ricerca intelligente di immagini”.

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Authors and Affiliations

  1. Department of Informatics, University of Bari “A. Moro”, Via Orabona, 4 - 70126, Bari, Italy

    Giovanna Castellano, Anna M. Fanelli & M. Alessandra Torsello

  2. Department of Computer Science, University of Milano, Via Bramante, 65 - 26013, Crema, Italy

    Gianluca Sforza

Authors
  1. Giovanna Castellano
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  2. Anna M. Fanelli
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  3. Gianluca Sforza
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  4. M. Alessandra Torsello
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Correspondence to Giovanna Castellano.

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Castellano, G., Fanelli, A.M., Sforza, G. et al. Shape annotation for intelligent image retrieval. Appl Intell 44, 179–195 (2016). https://doi.org/10.1007/s10489-015-0693-7

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