Image hashing basing on joint learning of multi-dimension features

For the problem of image retrieval in computer vision, basing on Principal Component Analysis (PCA) and convolution filtering, a novel image hashing realized by joint learning of multi-dimension features is proposed. As the first step, in order to get the convolution filters, the PCA eigenvectors is learned from the matrix constructed by the patches which are extracted randomly from original images. Next, in order to get the multi-dimension feature expression of the original images, the original images are convolution filtered to several groups which are in accordance with the filter sequence. Then, the hash projection matrix and binary coding are learned by a traditional hashing operator in each dimension respectively. Finally, the hash code of our joint learning model is obtained by merging the grouping binary coding together. Abundant experiment is done to validate the algorithm validity on a widely used dataset called CIFAR-10. The result shows that the algorithm of image hashing proposed in this paper performs well in image retrieval application, compare with the traditional image hashing, there is a certain performance improvement on both precision and recall.