Image hashing methods transform high-dimensional image features into low-dimensional binary codes while preserving semantic similarity. Among image hashing techniques, supervised image hashing approaches outperform unsupervised and semisupervised methods. However, labelling image data requires extra time and expert effort. In this study, we proposed a deep learning-based unsupervised image hashing method for unlabeled image data. The proposed hashing method is built in an end-to-end fashion. It consists of an encoder-decoder model. As a novel idea, we used a supervised pre-trained network as an encoder model, which provides fast convergence in the training phase and efficient image features. Hash codes are extracted by optimizing those intermediate features. Experiments performed on two benchmark image datasets demonstrate the competitive results compared to unsupervised image hashing methods.