Class consistent hashing for fast Web data searching

Hashing based ANN search has drawn lots of attention due to its low storage and time cost. Supervised hashing methods can leverage label information to generate compact and accurate hash codes and have achieved promising results. However, when dealing with the learning problem, most of existing supervised hashing methods are time-consuming and unscalable. To overcome these limitations, we propose a novel supervised hashing method named Class Consistent Hashing (CCH). In particular, CCH avoids using instance pairwise semantic similarity matrix which is widely used in existing methods. Instead, it uses class-pairwise semantic similarity whose size is far less than the former one, and generates hash codes for every class by optimizing the least-squares style objective function. Then, instances in the same class share the same class hash codes. Finally, we adopt a two-step hashing design strategy to learn the hash functions for out-of-sample instances. Experimental results on several widely used datasets illustrate that CCH can outperform several state-of-the-art shallow methods with the fastest training speed among supervised hashing methods.