A shape-based approach for leaf classification using multiscaletriangular representation

  • Authors:

  • Sofiene Mouine;Itheri Yahiaoui;Anne Verroust-Blondet

  • Affiliations:

  • Inria Paris-Rocquencourt, Paris, France;Inria Paris-Rocquencourt, Paris, France;Inria Paris-Rocquencourt, Paris, France

  • Venue:
  • Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
  • Year:
  • 2013

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Abstract

In this paper we introduce a new multiscale shape-based approach for leaf image retrieval. The leaf is represented by local descriptors associated with margin sample points. Within this local description, we study four multiscale triangle representations: the well known triangle area representation (TAR), the triangle side lengths representation (TSL) and two new representations that we denote triangle oriented angles (TOA) and triangle side lengths and angle representation (TSLA). Unlike existing TAR approaches, where a global matching is performed, the similarity measure is based on a locality sensitive hashing of local descriptors. The proposed approach is invariant under translation, rotation and scale and robust under partial occlusion. Evaluations made on four public leaf datasets show that our shape-based approach achieves a high retrieval accuracy w.r.t. state-of-art methods.