This is the implementation for the paper "Label Consistent Matrix Factorization based Hashing for Cross Modal Retrieval" ICIP 2017. The poster can be found here : https://sigport.org/documents/label-consistent-matrix-factorization-based-hashing-cross-modal-retrieval The main paper can be found here http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8296813 ******************************************************************************** Please download the datasets as instructed in the paper and put it in a folder titled datasets\ . For help regarding the datasets kindly please drop me a mail. ******************************************************************************** ******************************************************************************** The basic essence of my algorithm can be found in the two files [1] solveUCMFH_devraj3.m -- This is the ours1 implementation of our paper Kindly please select the parameters appropriately to get the best possible results. [2] solveUCMFH_devraj3_propagate -- This is the proposed techniue to handle large amounts of data. Kindly please the implementation in the mirflickr and nus-wide dataset codes. Also select the value of rho appropriately. ******************************************************************************** ******************************************************************************** I am providing the details of the operation for the three datasets (1) Wikipedia dataset -- please run wiki_ours1.m & wiki_ours2.m The ours2 version does not re-generate the hash codes of the retrieval set! so results are much better. (2) MirFlickr dataset -- please run the program mirflickr_ours2.m to get a sense of how to use the ours2 version of the algorithm. In this we initialize the first batch of the data using solveUCMFH_devraj3 and then propagate the learned variables through the code solveUCMFH_devraj3_propagate Kindly please select the value of rho appropriately. Also you need to select the subset size of the data appropriately. The larger the subset size the better is the overall performance though at the cost of computational power. I have not provided the code for the ours1 version for the MirFlickr dataset. It is quite easy to write it. Just take the number of samples N=5000 and use the solveUCMFH_devraj3.m to learn the hash codes as shown for the Wikipedia dataset. (3) Nuswide dataset -- The same as the MirFlickr dataset. Kindly please follow the same instructions! ******************************************************************************** ******************************************************************************** I am also providing here some extra stuff: Please look into the code wiki_extra_with_projections_and_stuff.m to understand the implementations (1) Suppose you make the latent factors V common -- use this codes solveUCMFH_devraj6_proj -- the basic implementation solveUCMFH_devraj6_proj_propagate -- the implementation to handle large amounts of data (2) Suppose you even make the U's common -- use this codes solveUCMFH_devraj7_proj -- the basic implementation use the following options option = 1 -- use pca projections option = 2 -- use cca projections option = 6 -- use random initializations/projections solveUCMFH_devraj7_proj_propagate -- the implementation to handle large amounts of data This extra implementations are not used in the ICIP paper but have been observed to give even better results as compared to what is reported. ********************************************************************************
devraj89/LABEL-CONSISTENT-MATRIX-FACTORIZATION-BASED-HASHING-FOR-CROSS-MODAL-RETRIEVAL
This code implements the paper "LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL" in ICIP 2017.
MatlabMIT