hashing-based similarity search is an important technique for large-scale query-by-example image retrieval system, since it provides fast search with computation and memory efficiency. however, it is a challenge work to design compact codes to represent original features with good performance. recently, a lot of unsupervised hashing methods have been proposed to focus on preserving geometric structure similarity of the data in the original feature space, but they have not yet fully refined image features and explored the latent semantic feature embedding in the data simultaneously. to address the problem, in this paper, a novel joint binary codes learning method is proposed to combine image feature to latent semantic feature with minimum encoding loss, which is referred as latent semantic minimal hashing. the latent semantic feature is learned based on matrix decomposition to refine original feature, thereby it makes the learned feature more discriminative. moreover, a minimum encoding loss is combined with latent semantic feature learning process simultaneously, so as to guarantee the obtained binary codes are discriminative as well. extensive experiments on several wellknown large databases demonstrate that the proposed method outperforms most state-of-the-art hashing methods.