A Bayesian Network Approach in the Relevance Feedback of Personalized Image Semantic Model

Based on a natural and friendly human-computer interaction, relevance feedback is used to determine a user’s requirement s and narrow the gap between low-level image features and high-level semantic concepts in order to optimize query result s and perform a personalized search. In this paper, we proposed a novel personalized approach for image semantic retrieval based on PISM (Personalized image semantic model), which use the user queries related to the image of feedback mechanism, dynamic image adjustment semantic similarity of the distribution, and fuzzy clustering analysis, PISM training model to make it more accurate expression of semantic image to meet the different needs of the user’s query. And the limitations of image-based semantic memory of learning algorithm, the initial experimental system developed by a number of user feedback to participate in relevant training, which analyzes the performance of the algorithm, the experiments show that the algorithm is a viable theory, with a value of the application.