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A shape-based approach for leaf classification using multiscale triangular representation

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.
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Submitted on : Friday, April 26, 2013 - 9:30:43 AM
Last modification on : Friday, January 21, 2022 - 3:15:57 AM
Long-term archiving on: : Saturday, July 27, 2013 - 4:04:34 AM


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  • HAL Id : hal-00818115, version 1



Sofiène Mouine, Itheri Yahiaoui, Anne Verroust-Blondet. A shape-based approach for leaf classification using multiscale triangular representation. ICMR '13 - 3rd ACM International Conference on Multimedia Retrieval, Apr 2013, Dallas, United States. ⟨hal-00818115⟩



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