Similarity learning for graph-based image representations

  • Authors:
  • Ciro de Mauro;Michelangelo Diligenti;Marco Gori;Marco Maggini

  • Affiliations:
  • Dipartimento di Ingegneria dell'Informazione, Università di Siena, Via Roma, 56-53100 Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Università di Siena, Via Roma, 56-53100 Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Università di Siena, Via Roma, 56-53100 Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Università di Siena, Via Roma, 56-53100 Siena, Italy

  • Venue:
  • Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
  • Year:
  • 2003

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Abstract

Visual database engines are usually based on predefined criteria for retrieving the images in response to a given query. In this paper, we propose a novel approach based on neural networks by which the retrieval criterion is derived on the basis of learning from examples. In particular, the proposed approach uses a graph-based image representation that denotes the relationships among regions in the image and on recursive neural networks which can process directed ordered acyclic graphs. The graph-based representation combines structural and subsymbolic features of the image, while recursive neural networks can discover the optimal representation for searching the image database. A set of preliminary experiments on artificial images clearly indicate that the proposed approach is very promising.