Identifying Paintings in Museum Galleries using Camera Mobile Phones

Boris Ruf Marcin Detyniecki 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This work focuses on the viability of using a cell-phone as mobile museum guidance. The integrated cell-phone camera is used to recognize the paintings in the gallery. The chosen solution is based on a client-server architecture and the object recognition is based on local features. The study focuses on the comparison, in terms of time and performance, of the Scale-Invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), the Nearest Neighbor Search (NNS) match and a k-means trees based search. It was found that SIFT outperforms SURF in terms of performance but is dominated in terms of time. Finally, the combination of SIFT and k-means based search provides a good compromise for the low-resolution images necessary in this setup. The study was performed using a windows mobile operated cell-phone and the 200 test images were taken on site from 4 different perspectives. The reference data set consisted of 1002 different art works of the Louvre.
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Conference papers
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Submitted on : Tuesday, April 5, 2016 - 11:25:30 AM
Last modification on : Thursday, March 21, 2019 - 1:11:46 PM


  • HAL Id : hal-01297997, version 1


Boris Ruf, Marcin Detyniecki. Identifying Paintings in Museum Galleries using Camera Mobile Phones. Singaporean French IPAL Symposium - SinFra'09, Feb 2009, Singapore, Singapore. ⟨hal-01297997⟩



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