This is the source code of our ICASSP 2012 paper "Cross-Modality Correlation Propagation for Cross-Media Retrieval", Please cite the following paper if you use our code.
Xiaohua Zhai, Yuxin Peng, and Jianguo Xiao, "Cross-Modality Correlation Propagation for Cross-Media Retrieval", 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2337-2340, Kyoto, Japan, Mar. 25-30, 2012. [PDF]
Run our script to train and test:
CMCP.m
The parameters are as follows:
I_tr: the feature matrix of image instances for training, dimension : tr_n * d_i
T_tr: the feature matrix of text instances for training, dimension : tr_n * d_t
I_te: the feature matrix of image instances for test, dimension : te_n * d_i
T_te: the feature matrix of text instances for test, dimension : te_n * d_t
trainCat: the category list of data for training, dimension : tr_n * 1
testCat: the category list of data for test, dimension : te_n * 1
alpha: parameter, default: 0.6
beta: parameter, default: 1
k: kNN parameter, default: 30
XMedia dataset can be downloaded from XMedia Dataset
For more information, please refer to our paper
If you are interested in cross-media retrieval, you can check our recently published overview paper on IEEE TCSVT:
Yuxin Peng, Xin Huang, and Yunzhen Zhao, "An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges", IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2017.[PDF]
Welcome to our Benchmark Website and Laboratory Homepage for more information about our papers, source codes, and datasets.