Current hashing-based image retrieval methods mostly assume that the database of images is static. However, this assumption is not true in cases where the databases are constantly updated (e.g. on Internet) and there exists the problem of concept drift. Online (a.k.a. incremental) hashing methods are proposed recently for image retrieval where the database is not static. However, they have not considered the concept drift problem. Moreover, they update hash functions dynamically by generating new hash codes for all accumulated data over time which is clearly uneconomical. In order to solve these two problems, Concept Preserving Hashing (CPH), is proposed. In contrast to existing methods, CPH preserves the original concept, i.e., the set of hash codes representing a concept is preserved over time, by learning a new set of hash functions to yield the same set of hash codes for images (old and new) of a concept. The objective function of CPH learning consists of three components: isomorphic similarity, hash codes partition balancing, and heterogeneous similarity fitness. Experimental results on 11 concept drift scenarios show that CPH yields better retrieval precisions than existing methods and does not need to update hash codes of previously stored images.
|Number of pages||14|
|Journal||IEEE Transactions on Cybernetics|
|Publication status||Accepted/In press - 18 Nov 2019|