Special Section on Image Processing for Cultural Heritage

Convolutional neural network for pottery retrieval

[+] Author Affiliations
Halim Benhabiles

Saad Dahlab University, Faculty of Sciences, LRDSI Laboratory, Route de Soumaa, BP270, Blida 09000, Algeria

Hedi Tabia

University of Cergy-Pontoise, ETIS/ENSEA, CNRS, 6, Avenue du Ponceau, 95014, Cergy-Pontoise, UMR 8051, France

J. Electron. Imaging. 26(1), 011005 (Oct 25, 2016). doi:10.1117/1.JEI.26.1.011005
History: Received May 30, 2016; Accepted October 5, 2016
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Abstract.  The effectiveness of the convolutional neural network (CNN) has already been demonstrated in many challenging tasks of computer vision, such as image retrieval, action recognition, and object classification. This paper specifically exploits CNN to design local descriptors for content-based retrieval of complete or nearly complete three-dimensional (3-D) vessel replicas. Based on vector quantization, the designed descriptors are clustered to form a shape vocabulary. Then, each 3-D object is associated to a set of clusters (words) in that vocabulary. Finally, a weighted vector counting the occurrences of every word is computed. The reported experimental results on the 3-D pottery benchmark show the superior performance of the proposed method.

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Citation

Halim Benhabiles and Hedi Tabia
"Convolutional neural network for pottery retrieval", J. Electron. Imaging. 26(1), 011005 (Oct 25, 2016). ; http://dx.doi.org/10.1117/1.JEI.26.1.011005


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