Kiranyaz S.Uhlmann S.Pulkkinen J.İnce, TürkerGabbouj M.2023-06-162023-06-1620119.78E+12https://doi.org/10.1109/INNOVATIONS.2011.5893823https://hdl.handle.net/20.500.14365/35692011 International Conference on Innovations in Information Technology, IIT 2011 -- 25 April 2011 through 27 April 2011 -- Abu Dhabi -- 85400In this paper, we propose an incremental evolution scheme within collective network of (evolutionary) binary classifiers (CNBC) framework to address the problem of incremental learning and to achieve a high retrieval performance for content-based image retrieval (CBIR). The proposed CNBC framework can still function even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be evolved incrementally. The CNBC framework basically adopts a "Divide and Conquer" type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. This design further allows such scalability that the CNBC can dynamically adapt its internal topology to new features and classes with minimal effort. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its efficiency and accuracy for scalable CBIR and classification. © 2011 IEEE.eninfo:eu-repo/semantics/closedAccessBinary classifiersContent-basedContent-Based Image RetrievalDivide and conquerEvolutionary searchGround truthImage databaseIncremental learningIts efficienciesPerformance evaluationRetrieval performanceEvolutionary algorithmsInnovationInformation technologyConference Object10.1109/INNOVATIONS.2011.58938232-s2.0-79959970952