SR-Clustering

Semantic Segmentation of events in egocentric lifelogging photo streams.

Requirements:

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)

Dataset

The training and validation of the code was performed using the EDUB-Seg dataset.

Citation

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).

Demo

Use code in Demo folder for a simple execution of our SR-Clustering algorithm (read Demo/README.txt before execution).