Bank of Quantization Models: A Data-Specific Approach to Learning Binary Codes for Large-Scale Retrieval Applications

We explore a novel paradigm in learning binary codes for large-scale image retrieval applications. Instead of learning a single globally optimal quantization model as in previous approaches, we encode the database points in a data-specific manner using a bank of quantization models. Each individual database point selects the quantization model that minimizes its individual quantization error. We apply the idea of a bank of quantization models to data independent and data-driven hashing methods for learning binary codes, obtaining state-of-the-art performance on three benchmark datasets.