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Comparative study of global invariant descriptors for object recognition

[+] Author Affiliations
Anant Choksuriwong

Prince of Songkhla University, Department of Computer Engineering, Laboratory iSys (Intelligent System), 90012 Hatyai, Songkhla, Thailand

Bruno Emile

Institut PRISME—UPRES EA 42 29, ENSI de Bourges—Université d’Orléans, 88 Boulevard Lahitolle, 18020 Bourges Cedex, France

Helene Laurent

Institut PRISME—UPRES EA 42 29, ENSI de Bourges—Université d’Orléans, 88 Boulevard Lahitolle, 18020 Bourges Cedex, France

Christophe Rosenberger

Laboratoire GREYC, ENSICAEN—Université de Caen—CNRS, 6 Boulevard Maréchal Juin, 14000 Caen Cedex, France

J. Electron. Imaging. 17(2), 023015 (May 14, 2008). doi:10.1117/1.2912071
History: Received February 21, 2007; Revised October 10, 2007; Accepted October 10, 2007; Published May 14, 2008
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Although many object invariant descriptors have been proposed in the literature, putting them into practice to obtain a robust recognition system that is able to face several perturbations is still a studied problem. After presenting the most commonly used global invariant descriptors, a comparative study permits us to show their ability to discriminate between objects with little training. The Columbia Object Image Library database (COIL-100), which presents a same object translated, rotated, and scaled, is used to test the invariant features of geometrical transforms. Partial object occultation or presence of complex background are examples of used images to test the robustness of the studied descriptors. We compare them in both a global and a local context (computed on the neighborhood of a pixel). The scale invariant feature transform descriptor is used as a reference for local invariant descriptors. This study shows the relative performance of invariant descriptors used in both a global and a local context and identifies the different situations for which they are best suited.

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© 2008 SPIE and IS&T

Citation

Anant Choksuriwong ; Bruno Emile ; Helene Laurent and Christophe Rosenberger
"Comparative study of global invariant descriptors for object recognition", J. Electron. Imaging. 17(2), 023015 (May 14, 2008). ; http://dx.doi.org/10.1117/1.2912071


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