We consider the problem of object segmentation in cultural sites. Since collecting and labeling large datasets of real images is challenging, we investigate whether the use of synthetic images can be useful to achieve good segmentation performance on real data. To perform the study, we collected a new dataset comprising both real and synthetic images of 24 artworks in a cultural site. The experimental results point out that the use of synthetic data helps to improve the performances of segmentation algorithms when tested on real images. Satisfactory performance is achieved exploiting semantic segmentation together with image-to-image translation and including a small amount of real data during training. The constributions of this work are the following:
We consider the cultural site Palazzo Bellomo of the EGO-CH dataset. The dataset has been acquired using a head-mounted Microsoft HoloLens device.
We have manual annotated 24 objects from 11 environments with semantic masks. In particular, we have annotated 4740 images from the training set of EGO-CH dataset and 848 images from the test set.
2. Synthetic domain
|Real Training Data||Accuracy%||Class Accuracy%||Mean IoU%||FWAVACC%|
F. Ragusa, D. DiMauro, A. Palermo, A. Furnari, G. M. Farinella. Synthetic vs Real. Objects Segmentation in Cultural Heritage. In International Conference on Pattern Recognition (ICPR), 2020. Download the paper.