Post Tuned Hashing

Learning to hash has proven to be an effective solution for indexing high-dimensional data by projecting them to similarity-preserving binary codes. However, most existing methods end up the learning scheme with a binarization stage, i.e. binary quantization, which inevitably destroys the neighborhood structure of original data. As a result, those methods still suffer from great similarity loss and result in unsatisfactory indexing performance. In this paper we propose a novel hashing model, namely Post Tuned Hashing (PTH), which includes a new post-tuning stage to refine the binary codes after binarization. The post-tuning seeks to rebuild the destroyed neighborhood structure, and hence significantly improves the indexing performance. We cast the post-tuning into a binary quadratic optimization framework and, despite its NP-hardness, give a practical algorithm to efficiently obtain a high-quality solution. Experimental results on five noted image benchmarks show that our PTH improves previous state-of-the-art methods by 13-58% in mean average precision.