Adaptive object recognition model using incremental feature representation and hierarchical classification

Sungmoon Jeong, Minho Lee

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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 languageEnglish
Pages (from-to)130-140
Number of pages11
JournalNeural Networks
Volume25
DOIs
StatePublished - Jan 2012

Keywords

  • Adaptive object recognition
  • Hierarchical feature classifier
  • Incremental feature representation
  • Incremental learning
  • Variant feature dimensions

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