Scene Classification Algorithm Based on Contextual Semantic Information Extended PLSA Model

In this paper, a novel scene classification algorithm based on contextual visual words combined with extended PLSA model is proposed in which the traditional visual words by contextual information are improved. First, the EICS-LBP (Edge Improved Center Symmetric Local Binary Pattern) and statistical domain color features are extracted from the edge dense sampling region and its 8 neighborhood followed by the computed normalized histogram intersection and the creation of the new feature by linear combination of local information and its contextual intersection information. The visual vocabulary is formed by clustering from dense sampling regions respectively. Then the Bag-Of-Words model is used to describe the image which gives us richer information of scene images and reduces ambiguities and errors. Finally, the potential semantics by extended PLSA model and the confusion table by KNN classifier are obtained. Experiment results show that this method could achieve a higher accuracy, especially performing well in multi-edge color images with contextual information. Besides, it does not require manual annotation on the scene.