An image retrieval method based on personalized image semantic model

This article introduced in the content-based image retrieval principle of image similarity measure, as well as visual feature extraction methods on the basis of the focus on the images and analysis of the semantic concept model, the typical extraction methods and algorithms. PISM (Personalized image semantic model), the use of 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.