A Novel Semantic Statistical Model for Automatic Image Annotation Using Ontology

Semantic annotation of images has emerged as an important research topic due to its potential application on both image understanding and database image search or web image search. Image annotation is a technique to choosing appropriate labels for images with extracting effective and hidden feature in pictures. In this paper we proposed method used combination of ImagNet ontology to has hierarchical classification and stochastic indexing that extract effective features by integrates visual topics (global distribution of topics over an image) and regional contexts (relationship between the regions) to automatic image annotation. Regional contexts and visual topics extracted from the image and are incorporated based on TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method. Regional contexts and visual topics are learned by PLSA (Probability Latent Semantic Analysis) from the training data. Experiments conducted on the 5k Corel dataset show the proposed method of image annotation in addition to reducing the complexity of the classification increased accuracy compared to the another methods.