IIAE CONFERENCE SYSTEM, The 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2013 (ICISIP2013)

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The Fribourg Product Image Database for Product Identification Tasks
Kai Chen, Jean Hennebert

Last modified: 2013-10-01


We present in this paper a new database containing images of end-consumer products. The database currently contains more than 3'000 pictures of products taken exclusively using mobile phones. We focused the acquisition on 3 families of product: water bottles, chocolate and coffee. Nine mobile phones have been used and about 353 different products are available. Pictures are taken in real-life conditions, i.e. directly in the shops and without controlling the illumination, centering of the product or removing the background. Each image is provided with ground truth information including the product label, mobile phone brand and series as well as region of interest in the images. The database is made freely available for the scientific community and can be used for content-based image retrieval benchmark dataset or verification tasks.


Image processing;CBIR;image retrieval;image database;FPID;benchmarking;product identification


  1. R. Data, D. Joshi, J. Li, and Z. Wang : “Image retrieval: Ideas, influences, and trends of the new age”, ACM Computing Surveys, Vol. 40, No. 2, pp. 5:1--5:60, 2008

  2. T. Deselaers : “Features for image retrieval”, Master's thesis, Human Language Technology and Pattern Recognition Group, RWTH Aachen University, Aachen, Germany, 2003
  3. J. Li, and J. Z. Wang : “Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 9, pp. 1075--1088, 2003

  4. M. Lux : “Content based image retrieval with LIRe”, MM '11 Proceedings of the 19th ACM international conference on Multimedia, pp. 735--738, 2011

  5. B. S. Manjunath, J. rainer Ohm, V. V. Vasudevan, and A. Yamada : “Color and Texture Descriptors”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11,pp. 703--715, 1988
  6. D. K. Park, Y. S. Jeon, and C. S. Won : “Efficient use of local edge histogram descriptor”, Proceedings of the 2000 ACM workshops on Multimedia, pp. 51--54, 2000

  7. G. Schaefer : “CVPIC Colour/Shape Histograms for Compressed Domain Image Retrieval”, 26th DAGM Symposium, pp. 424--431, 2004

  8. G. Schaefer, and M. Sitch : “UCID - An Uncompressed Colour Image Database”, Storage and Retrieval Methods and Applications for Multimedia, Vol. 5307, pp. 472--480, 2004

  9. H. Shao, T. Svoboda, and L. V. Gool : “ZuBuD --- Zürich buildings database for image based recognition”, Computer lecture notes on image retrieval and video retrieval, LNCS 2728, pp. 71--80, 2003

  10. H. Shao, T. Svoboda, T. Tuytelaars and, L. V. Gool : “HPAT indexing for fast object/scene recognition based on local appearance”, Proceedings of the 2nd internal conference on Image and video retrieval, pp. 71--80, 2003

  11. N. V. Shirahatti, and K. Barnard : “Evaluating Image Retrieval”, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01, pp. 955--961, 2005

  12. T. Sikora : “The MPEG-7 Visual Standard for Content Description – An Overview”, IEEE Trans. Circuits and Systems for Video Technology, Vol. 11, No. 6, pp. 696—702, 2001

  13. A. W. M. Smeulders, S. Member, M. Worring, S. Santini, A. Gupta, and R. Jain : “Content-based image retrieval at the end of the early years”, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 22, No. 12, pp. 1349--1380, 2000

  14. J. Z. Wang, J. Li, and Wiederhold : “SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, pp. 947--963, 2001

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