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.
Original language | English |
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Pages (from-to) | 130-140 |
Number of pages | 11 |
Journal | Neural Networks |
Volume | 25 |
DOIs | |
State | Published - Jan 2012 |
Keywords
- Adaptive object recognition
- Hierarchical feature classifier
- Incremental feature representation
- Incremental learning
- Variant feature dimensions