Compressed sensing based feature fusion for image retrieval

In the standard vector of locally aggregated descriptors (VLAD) model, the residual norm of each descriptor with corresponding center to the VLAD vector varies significantly, thus the visual similarity measure based on norm measurement will be corrupted. To address the problem, a weighted-residual is used in this paper to balance the distribution of residual norms of all descriptors in the VLAD vector to a certain degree. Moreover, to improve retrieval accuracy, local gradient and local color features of images are extracted and used to our proposed weighted VLAD method. Also, the global structural features and multiple weighted VLAD vectors are fused to represent an image. In order to better fuse these different image features, due to the advantage of collecting key information of original signal by compressed sensing (CS), it is used as a converter to transform the various features into a same feature subspace. Our proposed method is evaluated on three benchmark datasets, i.e., Holidays, Ukbench and Oxford5k. Experimental results show that our proposed method achieves good performances compared with other methods.