Robust Supervised Hashing

Hashing methods on large scale image retrieval have been extensively in attention. These methods can be roughly categorized as supervised and unsupervised. Unsupervised hashing methods mainly search for a projection matrix of the original data to preserve the Euclidean distance similarity, while supervised hashing methods aim to preserve the label similarity. However, most hashing methods propose a complicated objective function and search for optimized or relaxed solutions. Some methods will consume much time to train a good binary code. This paper is not focusing on formulating a complex solution like the previous state-of-art methods. Contrarily, we firstly propose a simple objective function on supervised hashing as far as we have learned. And we devise a novel solution which uses a maximum and equal Hamming distance code to construct the label information. This method keeps a comparable accuracy with the state-of-the-art supervised hashing methods.