This software package provides baseline scores for the 4 different computer vision challenges introduced in our ICMR paper: The Rijksmuseum Challenge: Museum-Centered Visual Recognition
In our paper, we have introduced 4 challenges:
- Creator: predict the creator of a piece of art
- Material: predict the materials used (pen, paper)
- Type: predict the type of art (drawing, painting, sculpture)
- Year: predict the year of creation (1960, 1684) See the paper for more details and considerations about the used data.
These provide Matlab codes are intended to showcase the baselines on the Rijksmuseum Challenge 2014 as published in the paper.
Per challenge there is a file (ie exp_rijks_creator.m for the Creator Challenge), which
- loads the data
- loads/trains SVM models
- cross-validate the SVM hyperparameter
- evaluate the model using the challenge specific measure (MCA, mAP, Sq-Loss)
The expected results are included in each of the challenge scripts. For example for the Creator Challenge:
| Setting | Set | Num Classes (%) | MCA Top 1 | Top 2 | Top 3 | Top 4 | Top 5 |
|---|---|---|---|---|---|---|---|
| 0 | VAL | 375 ( all ) | 50.27 | 67.85 | 73.16 | 76.62 | 78.87 |
| 0 | TST | 375 ( all ) | 51.02 | 68.42 | 73.98 | 77.60 | 79.97 |
| 1 | TST | 374 ( 59.1) | 65.53 | 73.33 | 77.26 | 79.78 | 81.33 |
| 2 | TST | 300 ( 55.5) | 67.63 | 75.38 | 78.84 | 81.14 | 82.63 |
| 3 | TST | 250 ( 52.5) | 69.45 | 77.04 | 80.68 | 82.92 | 84.31 |
| 4 | TST | 200 ( 48.7) | 71.17 | 79.15 | 82.77 | 84.88 | 86.22 |
| 5 | TST | 150 ( 43.6) | 72.58 | 80.90 | 84.57 | 86.76 | 88.21 |
| 6 | TST | 100 ( 36.8) | 75.73 | 83.42 | 87.32 | 89.14 | 90.45 |
| 7 | TST | 50 ( 26.4) | 78.18 | 86.45 | 90.00 | 91.95 | 93.22 |
| 8 | TST | 25 ( 18.7) | 81.81 | 89.59 | 92.79 | 94.69 | 96.01 |
These results correspond to the results of the intensity FV in Table 1 in [mensink14icmr].
The provided Matlab code, makes use of the LIBLINEAR package (web,github).
For convenience the required train function is added to the lib directory (for Mac and Linux only). Ensure to add it to the Matlab path
The data (Fisher Vector files, ground-truth, images, and xml files) is available from: figshare (doi)
The Fisher Vectors are extracted with the FVKit.
When using this code, or this challenge, please cite the following paper (pdf)
@INPROCEEDINGS{mensink14icmr,
author = {Thomas Mensink and Jan van Gemert},
title = {The Rijksmuseum Challenge: Museum-Centered Visual Recognition},
booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)},
year = {2014}
}
- V1.0, Nov 2017: Moved to Github (initial release)
- V1.1, Jan 2018: Updated readme and make compatible with FVKit extraction.
Thomas Mensink, University of Amsterdam, thomas.mensink@uva.nl