A novel long-term learning algorithm for relevance feedback in content-based image retrieval
Relevance feedback has proven to be a powerful tool to bridge the semantic gap between low-level features and high-level human concepts in content-based image retrieval (CBIR). However, traditional short-term relevance feedback technologies are confined to using the current feedback record only. Log-based long-term learning captures the semantic relationships among images in a database by analyzing the historical relevance information to boost the retrieval performance effectively. In this paper, we propose an expanded-judging model to analyze the historical log datas semantic information and to expand the feedback sample set from both positive and negative relevant information. The index table is used to facilitate the log analysis. The expanded-judging model is applied in image retrieval by combining with short-term relevance feedback algorithms. Experiments were carried out to evaluate the proposed algorithm based on the Corel image database. The promising experimental results validate the effectiveness of our proposed expanded-judging model.
long-term learning relevance feedback content-based image retrieval semantic gap log data