Adaptive object recognition model using incremental feature representation and hierarchical classification

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.

► We propose an adaptive object recognition model. ► It includes an incremental feature representation and a hierarchical classifier. ► The incremental feature representation offers plasticity to accommodate new objects. ► The hierarchical classifier reduces the forgetting problem of learnt objects. ► The proposed model recognizes object classes with enhanced stability and flexibility.