Convolutional neural network for pottery retrieval

Halim Benhabiles; Hedi Tabia

doi:10.1117/1.JEI.26.1.011005

25 October 2016 Convolutional neural network for pottery retrieval

Halim Benhabiles, Hedi Tabia

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 1, 011005 (October 2016). https://doi.org/10.1117/1.JEI.26.1.011005

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.

Citation Download Citation

Halim Benhabiles and Hedi Tabia "Convolutional neural network for pottery retrieval," Journal of Electronic Imaging 26(1), 011005 (25 October 2016). https://doi.org/10.1117/1.JEI.26.1.011005

Published: 25 October 2016

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