Dynamic semantic feature-based long-term cross-session learning approach to content-based image retrieval

This paper proposes a novel content-based image retrieval technique, which facilitates short-term (intra-query) and long-term (inter-query) learning processes by integrating accumulated users' historical relevance feedback-based semantic knowledge. The history is efficiently represented as a dynamic semantic feature of the images. As such, the high-level semantic similarity measure can be dynamically adapted based on the semantic relevance derived from the dynamic semantic features. The short-term relevance feedback technique can benefit from long-term learning. Our extensive experiments show that the proposed system outperforms three peer systems in the context of both correct and erroneous relevance feedback.