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.
The code uses the following software and data to run:
- MATLAB (Windows, Unix version is the same)
- An approximated version of Dr. Hardoon's KCCA code package. (4.3 KB)
- Descriptors (PRID) (152 MB)
- 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
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]
> ...
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].
- 1.0 March. 2017 - Initial Release
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}, }The system has been tested on Linux and Mac only. We expect it should run smoothly on Windows with a small effort.
[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.
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.