Image Classification Based on pLSA Fusing Spatial Relationships Between Topics

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.