Research Of Image Retrieval Based On Relevance Feedback

To solve the semantic gap problem between low-level visual features in images and high-level human language in content-based image retrieval system better, it makes users easier to participate into the retrieval process by adding relevance feedback module to the basic retrieval module. When uses present judgment for previous retrieval result to help machine learning and better understanding the users' intention, it will guess the intention more accurately.In this relevance feedback model, the feature vectors are extracted in five sub-blocks, the color features are extracted by histogram, the texture features are extracted by using edge histogram descriptor based on MPEG-7, the feature space is builded with multivariate normal distribution, the dynamic weights of the internal vectors are reckoned using LOGISTIC regression model, and finally Bayesian posterior is used to estimate posterior probabilities of all images which shows how the retrieved images after adjusting weights meet the users' requirement. Correlation images are outputted according to the probabilities in descending order. The main research works are:1. Learning the current image feature extraction methods, especially color and texture feature extraction algorithm, extracting two different dimensions of two kind of features is more efficient.2. The LOGISTIC regression model used to calculate the dynamic weight of the initial feature vectors is choosen after searching for relevant content about dynamic adjustment of feature weights.3. After adjusting the weights, using Bayesian estimation model to calculate the predicted probabilities, images are exported on the basis of predicted probabilities instead of the sequence similarities.4. The experiments are done by matlab and Java softwares and the results show that this method has better recall and precision. The average recall is 0.80 and the average precision is 0.85.