Comparative Study Of Local Descriptors For Measuring Object Taxonomy

Baptiste Hemery (1) , Hélène Laurent (2) , Bruno Emile (2) , Christophe Rosenberger (1)

1 Equipe SAFE - Laboratoire GREYC - UMR6072
2 PRISME - Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique

Many object descriptors have been proposed in the state of the art. For many reasons (occlusion, point of view, acquisition conditions. . . ), local descriptors have a better robustness for image understanding applications. The goal of this paper is to make a comparative study of eight recent local descriptors. The objective is here to quantify their ability to generate automatically an object taxonomy. In order to answer this question, we use the Caltech256 benchmark which provides a large object taxonomy used as reference. This study shows that SIFT, di erential invariants and shape context descriptors are the best ones to achieve this goal.

Domaines: Cryptographie et sécurité [cs.CR]

Baptiste Hemery, Hélène Laurent, Bruno Emile, Christophe Rosenberger. Comparative Study Of Local Descriptors For Measuring Object Taxonomy. IEEE International Conference on Image and Graphics (ICIG), 2009, France. ⟨10.1109/ICIG.2009.38⟩. ⟨hal-00989887⟩