Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification
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README.md

MCK-CCA: Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification

This repository provides the implementation of our MCK-CCA approach presented in the paper Giuseppe Lisanti, Svebor Karaman, Iacopo Masi, "Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification”, ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), in press, 2017.

This work is an extension of our previous method [2], that was made available at KCCA Re-Id. Our approach, illustrated below, obtains state-of-the-art performance on multiple Re-Identification benchmarks thanks to the use of a powerful descriptor, the learning of multiple common kernelized projection spaces and an iterative logistic regression to select and weight the distances estimated in these spaces.

MCK-CCA

Requirements

The code uses the following software and data to run:

  1. MATLAB (Windows, Unix version is the same)
  2. An approximated version of Dr. Hardoon's KCCA code package. (4.3 KB)
  3. Descriptors (PRID) (152 MB)
  4. Logistic Regression (liblinear)

Jan. 2017: The code will download and compile all the necessary files. We are using an approximated, customized version of the KCCA package from Hardoon, which original license is non-commercial.

MATLAB should be properly configured to compile MEX files, it can be as easy as running the following command in MATLAB:

>> mex -setup

Demo Example

To run our code just run Demo_MCKCCA.m and you should see something like this:

>Trial # 3
>> Fold # 1
>Learning KCCA [Desc 1: Kernel Linear]
>Centering Kx and Ky
>Decomposing Kernel with PGSO
>Computing nbeta from nalpha
>Project train and test [Desc 1: Kernel Linear]
>Learning KCCA [Desc 1: Kernel Gauss]
> ...

Person Representation

The person representation is derived from KCCA Re-Id but in MCK-CCA each feature extracted in each region is kept independent as a channel. For each channel, a specific KCCA is estimated. For more information on the person representation used see [1].

Changelog

  • 1.0 March. 2017 - Initial Release

Citation

Please cite these two papers using the following bibtex if you use our code:

@article{lisanti:mckcca:tomm17,
author = {Lisanti, Giuseppe and Karaman, Svebor and Masi, Iacopo},
title = {Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification},
booktitle = {ACM Transactions on Multimedia Computing, Communications and Applications (TOMM)},
year = {2017}, }

and

@article{lisanti:icdsc14,
author = {Lisanti, Giuseppe and Masi, Iacopo and {Del Bimbo}, Alberto},
title = {Matching People across Camera Views using Kernel Canonical Correlation Analysis},
booktitle = {Eighth ACM/IEEE International Conference on Distributed Smart Cameras},
year = {2014}, }

Troubleshooting

The system has been tested on Linux and Mac only. We expect it should run smoothly on Windows with a small effort.

References

[1] G. Lisanti , S. Karaman, I. Masi, Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification, ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) , 2017.

[2] G. Lisanti , I. Masi , A. Del Bimbo, Matching People across Camera Views using Kernel Canonical Correlation Analysis”, Eighth ACM/IEEE International Conference on Distributed Smart Cameras, 2014.

[3] G. Lisanti, I. Masi, A. D. Bagdanov, and A. Del Bimbo, "Person Re-identification by Iterative Re-weighted Sparse Ranking", IEEE Transactions on Pattern Analysis and Machine Intelligence 2014.

License

MCK-CCA code is Copyright (c) 2014-2017 of Giusppe Lisanti and Iacopo Masi and Svebor Karaman giuseppe.lisanti@unipv.it, iacopoma@usc.edu, svebor.karaman@columbia.edu. Media Integration and Communication Center (MICC), University of Florence.