Unsupervised Deep Embedded Hashing for Large-Scale Image Retrieval

抄録

Hashing methods have proven to be effective algorithm for image retrieval. However, learning discriminative hash codes is challenging for unsupervised models. In this paper, we propose a novel distinguishable image retrieval framework, named Unsupervised Deep Embedded Hashing (UDEH), to recursively learn discriminative clustering through soft clustering models and generate highly similar binary codes. We reduce the data dimension by auto-encoder and apply binary constraint loss to reduce quantization error. UDEH can be jointly optimized by standard stochastic gradient descent (SGD) in the embedd layer. We conducted a comprehensive experiment on two popular datasets.