Integrated visual vocabulary in latent Dirichlet allocation-based scene classification for IKONOS image

Retno Kusumaningrum, Hong Wei, Ruli Manurung, Aniati Murni Arymurthy

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

Scene classification based on latent Dirichlet allocation (LDA) is a more general modeling method known as a bag of visual words, in which the construction of a visual vocabulary is a crucial quantization process to ensure success of the classification. A framework is developed using the following new aspects: Gaussian mixture clustering for the quantization process, the use of an integrated visual vocabulary (IVV), which is built as the union of all centroids obtained from the separate quantization process of each class, and the usage of some features, including edge orientation histogram, CIELab color moments, and gray-level co-occurrence matrix (GLCM). The experiments are conducted on IKONOS images with six semantic classes (tree, grassland, residential, commercial/industrial, road, and water). The results show that the use of an IVV increases the overall accuracy (OA) by 11 to 12% and 6% when it is implemented on the selected and all features, respectively. The selected features of CIELab color moments and GLCM provide a better OA than the implementation over CIELab color moment or GLCM as individuals. The latter increases the OA by only ~2 to 3%. Moreover, the results show that the OA of LDA outperforms the OA of C4.5 and naive Bayes tree by ~20%.

Original languageEnglish
Article number083690
JournalJournal of Applied Remote Sensing
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • IKONOS
  • bag of visual words
  • integrated visual vocabulary
  • latent Dirichlet allocation
  • scene classification

Fingerprint Dive into the research topics of 'Integrated visual vocabulary in latent Dirichlet allocation-based scene classification for IKONOS image'. Together they form a unique fingerprint.

Cite this