Image Classification Based on pLSA Fusing Spatial Relationships Between Topics

Author

Jin, Biao ; Hu, Wenlong ; Wang, Hongqi

Author_Institution

Key Lab. of Technol. in Geo-spatial Inf. Process. & Applic. Syst., Inst. of Electron., Beijing, China

Volume

19

Issue

3

fYear

2012

fDate

3/1/2012 12:00:00 AM

Firstpage

151

Lastpage

154

Abstract

The spatial relationships between objects are the important specificities of the images. This letter proposes a histogram to represent the spatial relationships, and use fuzzy k-nearest neighbors (k-NN) classifier to classify the spatial relationships (left, right, above, below, near, far, inside, outside) with soft labels. Then probabilistic latent semantic analysis (pLSA) is extended by taking into account the spatial relationships between topics (SR-pLSA), and SR-pLSA is used to model the image as the input for support vector machine (SVM) to classify the scene. Experiments demonstrate that the proposed method can achieve high classification accuracy.

Keywords

fuzzy set theory; image classification; image fusion; probability; support vector machines; SR-pLSA; fuzzy k-nearest neighbor classifier; high classification accuracy; image classification; image specificity; pLSA fusing spatial relationship representation; probabilistic latent semantic analysis; support vector machine; Analytical models; Histograms; Materials; Probabilistic logic; Semantics; Support vector machines; Vectors; Image classification; probabilistic latent semantic analysis (pLSA); spatial relationship;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2012.2184091

Filename

6129479