Volume XLII-2/W15
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 585–590, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-585-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 585–590, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-585-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Aug 2019

22 Aug 2019

ARTIFICIAL INTELLIGENCE AS A LOW-COST SOLUTION FOR MUSEUM VISIT DIGITAL CONTENT ENRICHMENT: THE CASE OF THE FOLKLORE MUSEUM OF XANTHI

G. Ioannakis1,2, L. Bampis1,3, and A. Koutsoudis1 G. Ioannakis et al.
  • 1Athena Research and Innovation Centre, Xanthi’s Division, PO Box 159, Kimmeria Campus, 67100 Xanthi, Greece
  • 2Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, Greece
  • 3Department of Production and Management Engineering, Democritus University of Thrace, Xanthi 67100, Greece

Keywords: Content Enrichment, Expedition-Center Visits, Mobile Devices, Hybrid Recognition, Machine Learning, Convolutional Neural Networks, Bags of Visual Words

Abstract. The on-demand content enrichment of an exhibition center visit is an active applied research domain. This work focuses on the exploitation of mobile devices as an efficient medium to deliver information related to an exhibit or an area within the exhibition center by utilizing machine learning approaches. We present YPOPSEI, an integrated system that formulates the information retrieval task as an image recognition mechanism, enabling visitors to simply capture an entity of interest in order to acquire information similar to a tour-guidance experience via their personal mobile devices. This scheme not only minimizes the additional infrastructure requirements, but additionally enhances the versatility in cases of exhibits topology alterations while still providing high accuracy in terms of image content recognition. Two hybrid approaches are developed that set Convolutional Neural Networks (CNNs) and Bags of VisualWords (BOVWs) to operate in a synergistic and cooperative manner. They are evaluated under real-world conditions on a client-server Web architecture system that experimentally operates within the premises of the Folklore Museum of Xanthi, Greece.