Local N-ary Patterns: A local multi-modal descriptor for place categorization

Hojung Jung, Oscar Martinez Mozos, Yumi Iwashita, Ryo Kurazume

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

This paper presents an effective integration method of multiple modalities such as depth, color, and reflectance for place categorization. To achieve better performance with integrated multi-modalities, we introduce a novel descriptor, local N-ary patterns (LTP), which can perform robust discrimination of place categorization. In this paper, the LNP descriptor is applied to a combination of two modalities, i.e. depth and reflectance, provided by a laser range finder. However, the LNP descriptor can be easily extended to a larger number of modalities. The proposed LNP describes relationships between the multi-modal values of pixels and their neighboring pixels. Since we consider the multi-modal relationship, our proposed method clearly demonstrates more effective classification results than using individual modalities. We carried out experiments with the Kyushu University Indoor Semantic Place Dataset, which is publicly available. This data-set is composed of five indoor categories: corridors, kitchens, laboratories, study rooms, and offices. We confirmed that our proposed method outperforms previous uni-modal descriptors.

Original languageEnglish
Pages (from-to)402-415
Number of pages14
JournalAdvanced Robotics
Volume30
Issue number6
DOIs
Publication statusPublished - Mar 18 2016

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Local N-ary Patterns: A local multi-modal descriptor for place categorization'. Together they form a unique fingerprint.

Cite this