Software Workshop

The Columbia Computer Center IBM Machine Room, about 1980
Photo: Bob Resnikoff

Our goal is to increase the impact of the research made at the Max Planck Institute for Intelligent Systems by:

improving software that demonstrate a scientific outcome (eg. speeding up, port to a new framework, ...)
helping scientists write good software (teaching, training or consulting),
providing scientists with good infrastructure, good practices and tutorials,
disseminating expertise through code reviews and software documentation,
assisting scientists and departments or research groups in fields related to software engineering to ease the deployment, maintenance, or acceptance of a software
developing tools that improve the workflow of scientists

Fields of expertise:

Programming languages: C++, Python, CMake, CUDA, OpenCL, C#, Java, Matlab, R ... bash & powershell
Technologies: Django (+ celery + rest), Qt, OpenCV, Ansible, Kinect2, Caffe2, iOS, Android
Methodology: Scrum

Members:

Raffi Enficiaud

Senior Research Engineer @ Software Workshop

	Tübingen

	+49 7071 601 1756

Jekaterina Kaparina

	Tübingen

Jean-Claude Passy

Research Engineer @ Software Workshop

	Tübingen

	+49 7071 601 1851

Abhinanda Ranjit Punnakkal

	Tübingen

Nitin Saini

	Tübingen

Alumni:

Robust and scalable PCA using Grassmann averages

2014-06-01

The Grassmann Averages PCA is a method for extracting the principal components from a sets of vectors, with the nice following properties: 1) it is of linear complexity wrt. the dimension of the vectors and the size of the data, which makes the method highly scalable, 2) It is more robust to outliers than PCA in the sense that it minimizes an L1 norm instead of the L2 norm of the standard PCA. It comes with two variants: 1) the standard computation, that coincides with the PCA for normally distributed data, also referred to as the GA, 2) a trimmed variant, that is more robust to outliers, referred to the TGA. We provide implementations for the Grassmann Average, the Trimmed Grassmann Average, and the Grassmann Median. The simplest is the Matlab implementation used in the CVPR 2014 paper, but we also provide a faster C++ implementation, which can be used either directly from C++ or through a Matlab wrapper interface. The repository contains the following:

a C++ multi-threaded implementation of the GA and TGA
a C++ multi-threaded implementation of the EM-PCA (for comparisons)
binaries that computes the GA, TGA and EM-PCA on a set of images (frames of a video)
Matlab bindings
Documentation of the C++ API

https://ps.is.tuebingen.mpg.de/research_projects/robust-pca
https://github.com/MPI-IS/Grassmann-Averages-PCA

code-doc

2014-06-01

A small Django application for hosting documentation and artifacts, generated from source code and for easy access.

https://bitbucket.org/renficiaud/code_doc/

django continuous-integration documentation python

livius

The tool we developed for generating the videos on MLSS 2015

https://github.com/raffienficiaud/livius

python batch processing video ffmpeg

3 results

2015

Scalable Robust Principal Component Analysis using Grassmann Averages

Hauberg, S., Feragen, A., Enficiaud, R., Black, M.

IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015 (article)

Abstract

In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

preprint pdf from publisher supplemental Project Page [BibTex]