Extended biologically inspired model for object recognition based on oriented Gaussian-Hermite moment

Yan Feng Lu, Hua Zhen Zhang, Tae Koo Kang, In Hwan Choi, Myo Taeg Lim

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Hierarchical Model and X (HMAX) presents a biologically inspired model for robust object recognition. The HMAX model, based on the mechanisms of the visual cortex, can be described as a four-layer structure. Although the performance of HMAX in object recognition is robust, it has been shown to be sensitive to rotation, which limits the model[U+05F3]s performance. To alleviate this limitation, we propose an Oriented Gaussian-Hermite Moment-based HMAX (OGHM-HMAX). In contrast to HMAX which uses a Gabor filter for local feature representation, OGHM-HMAX employs the Oriented Gaussian-Hermite Moment (OGHM), which is a local representation method that represents features and is robust against distortions. OGHM is an extension of the modified discrete Gaussian-Hermite moment (MDGHM). To show the effectiveness of the proposed method, experimental studies on object categorization are conducted on the CalTech101, CalTech5, Scene13 and GRAZ01 databases. Experimental results demonstrate that the performance of OGHM-HMAX is a significant improvement on that of the conventional HMAX.

Original languageEnglish
Pages (from-to)189-201
Number of pages13
JournalNeurocomputing
Volume139
DOIs
Publication statusPublished - 2014 Sep 2

Keywords

  • Classification
  • Gabor features
  • HMAX
  • Object recognition
  • Oriented Gaussian-Hermite moment

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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