Block-based long-term content-based image retrieval using multiple features

This paper proposes a novel content-based image retrieval technique, which integrates block-based visual features and user's query concept-based semantic features. It also facilitates short-term and long-term learning processes by integrating users' historical relevance feedback information. The history is compactly stored in a semantic feature matrix and efficiently represented as semantic features of the images. The short-term relevance feedback technique can benefit from long-term learning. The high-level semantic features are dynamically updated based on users' query concept and therefore represent the image's semantic meaning more accurately. Our extensive experimental results demonstrate that the proposed system outperforms its seven state-of-the-art peer systems in terms of retrieval precision and storage space.