title: Adaptive object recognition model using incremental feature representation and hierarchical classification.
abstract: This paper presents an adaptive object recognition model based on incremental feature representation and a hierarchical feature classifier that offers plasticity to accommodate additional input data and reduces the problem of forgetting previously learned information. The incremental feature representation method applies adaptive prototype generation with a cortex-like mechanism to conventional feature representation to enable an incremental reflection of various object characteristics, such as feature dimensions in the learning process. A feature classifier based on using a hierarchical generative model recognizes various objects with variant feature dimensions during the learning process. Experimental results show that the adaptive object recognition model successfully recognizes single and multiple-object classes with enhanced stability and flexibility.
authors.affiliation: School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, South Korea.
authors.forename: Sungmoon
authors.initials: S
authors.lastname: Jeong
authors.forename: Minho
authors.initials: M
authors.lastname: Lee
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D010364:Pattern Recognition, Visual / Q000145:classification* / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods*