A hybrid holistic/semantic approach for scene classification

Zenghai CHEN, Zheru CHI, Hong FU

Research output: Chapter in Book/Report/Conference proceedingChapters

3 Citations (Scopus)

Abstract

There are two main strategies to tackle scene classification: holistic and semantic. The former characterizes a scene using its global features, while the latter represents a scene by modeling its internal object configuration. Holistic strategy is good at representing scenes with simple contents, but it does not represent well complex scenes that consist of multiple objects. By contrast, semantic strategy is advantageous at recognizing scenes with complex objects, but it does not work well for simple scenes. In this paper, we propose to integrate holistic and semantic strategies to cope with scene classification. In particular, we exploit a deep learning algorithm to learn features for scene representation in the holistic way. For the semantic strategy, we explore a semantic spatial pyramid to represent the spatial object configuration of scenes. The holistic and semantic strategies are integrated using a method proposed by us. Experimental results on a benchmark natural scene dataset demonstrate the effectiveness of our proposed hybrid approach for scene classification, by comparing to several state-of-the-art algorithms. Copyright © 2014 IEEE.
Original languageEnglish
Title of host publication2014 22nd International Conference on Pattern Recognition (ICPR 2014)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages2299-2304
ISBN (Electronic)9781479952090
ISBN (Print)9781479952083
DOIs
Publication statusPublished - 2014

Citation

Chen, Z., Chi, Z., & Fu, H. (2014). A hybrid holistic/semantic approach for scene classification. In 2014 22nd International Conference on Pattern Recognition (ICPR 2014) (pp. 2299-2304). Piscataway, NJ: IEEE.

Keywords

  • Scene classification
  • Holistic representation
  • Semantic representation
  • Semantic spatial pyramid

Fingerprint Dive into the research topics of 'A hybrid holistic/semantic approach for scene classification'. Together they form a unique fingerprint.