Title :
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
fDate :
3/1/2012 12:00:00 AM
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;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2012.2184091
