Abstract
In order to solve the low discrimination of image representations in complicated duplicate image detection, this paper presents a complicated duplicate image representation approach based on descriptor learning. This approach firstly formulates objective function as minimizing empirical error on the labeled data. Then the tag matrix and the classification matrix of training dataset are brought into the objective function to ensure semantic similarity. Finally, by relaxing the constraints, we can get the learning hashes. The learning hashes are used to quantify local descriptors of images into binary codes and the frequency histograms of binary codes are as image representations. Experimental results demonstrate that compared with the state-of-the-art algorithms, this approach can effectively improve the discrimination of image presentations by introducing semantic information.