Semantic Segmentation of events in egocentric lifelogging photo streams.
1) Caffe Deep Learning Framework and matcaffe wrapper (for global features calculation)
Caffe main page: http://caffe.berkeleyvision.org/
Good Linux installation tutorial: https://github.com/tiangolo/caffe/blob/ubuntu-tutorial-b/docs/install_apt2.md
CaffeNet model: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
2) IMAGGA account (for semantic features calculation)
http://www.imagga.com/
2) [ALTERNATIVE] if using LSDA instead of IMAGGA, download and install the needed files to ./LSDA from the GitHub repository
[https://github.com/jhoffman/lsda](https://github.com/jhoffman/lsda)
3) Compile files in GCMex for your system.
4) MATLAB
5) [IMAGGA only] Python 2.7 (with nltk libraries)
The training and validation of the code was performed using the EDUB-Seg dataset.
If you use this code or the dataset, please cite the following papers:
Dimiccoli, M., Bolaños, M., Talavera, E., Aghaei, M., Nikolov, S. & Radeva, P. (2015)
"SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation".
Submitted to Pattern Recognition. Pre-print: http://arxiv.org/abs/1512.07143
Talavera, E., Dimiccoli, M., Bolaños, M., Aghaei, M., & Radeva, P. (2015).
“R-Clustering for Egocentric Video Segmentation”.
In 7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA).
Use code in Demo folder for a simple execution of our SR-Clustering algorithm (read Demo/README.txt before execution).