Incremental hashing with dynamic semantic pool

Most of the existing hashing methods for image retrieval are based on the assumption the image database is stationary. However, in the real world data environments are always changing or non-stationary, therefore the underlying data distribution may change from time to time which will result in the problem of concept drift. Incremental Hashing (ICH) is the only existing method to handle image retrieval with concept drift in non-stationary data environments. It builds hash codes for the database through increments. At each increment, a set of new hash functions is built with the new chunk of data, which is utilized to update the multi-hashing system to generate multiple sets of hash codes for all data. However, only the newest data chunk is used to train individual hash functions, while the semantic similarity information of previous data is missed. In this paper, we present a new hashing method based on ICH for image retrieval with concept drift, Incremental Hashing with Dynamic Semantic Pool (ICH-DSP). It builds a semantic pool to collect representative labeled data for each existing class. The semantic pool is updated incrementally and is used as the supervisory information for the training of hash functions. Experimental results on three real world image databases show that ICH-DSP outperforms the original ICH and other state-of-the-art hashing methods.