Robust Discrete Spectral Hashing for Large-Scale Image Semantic Indexing

In big data era, the ever-increasing image data has posed significant challenge on modern image retrieval. It is of great importance to index images with semantic keywords efficiently and effectively, especially confronted with fast-evolving property of the web. Learning-based hashing has shown its power in handling large-scale high-dimensional applications, such as image retrieval. Existing solutions normally separate the process of learning binary codes and hash functions into two independent stages to bypass challenge of the discrete constraints on binary codes. In this work, we propose a novel unsupervised hashing approach, namely robust discrete hashing (RDSH), to facilitate large-scale semantic indexing of image data. Specifically, RDSH simultaneously learns discrete binary codes as well as robust hash functions within a unified model. In order to suppress the influence of unreliable binary codes and learn robust hash functions, we also integrate a flexible $ell _{2,p}$ loss with nonlinear kernel embedding to adapt to different noise levels. Finally, we devise an alternating algorithm to efficiently optimize RDSH model. Given a test image, we first conduct $r$-nearest-neighbor search based on Hamming distance of binary codes, and then propagate semantic keywords of neighbors to the test image. Extensive experiments have been conducted on various real-world image datasets to show its superiority to the state-of-the-arts in large-scale semantic indexing.