Incremental Hashing with Undersampling

Abstract

Most of current hashing methods are proposed based on the assumption that the database is stationary. However, this assumption is not always true as the data environment is sometimes non-stationary. When new images being added to the database, data distributions of existing classes may change and new classes may also appear which result in concept drifts. The problem of concept drifts is unavoidable in non-stationary data environments. Incremental Hashing (ICH) is an effective method for image retrieval in non-stationary data environments with concept drifts using multiple hash tables. In ICH, new concept is adapted by training new hash table using the most updated data chunks. However, images in the new data chunk may not be all informative for updating. To enhance the efficiency of ICH, ICH with Undersampling (ICHUS) is proposed to select informative samples in the new data chunk for the training of new hash table to adapt to the non-stationary data environment. Experimental results show that ICHUS yields a better retrieval performance than ICH and state-of-art non-stationary hashing methods.