Multilayer semantic representation learning for image retrieval

Long-term relevance feedback learning is an important learning mechanism in content-based image retrieval. In this paper, our work has two contributions: (1) A multilayer semantic representation (MSR) is proposed and an algorithm is implemented to automatically build the MSR for image database through long-term relevance feedback learning. (2) The accumulated MSR is incorporated with the short-term feedback learning to help subsequent users´ retrieval. The MSR memorizes the multicorrelation among images and integrates these memories to build hidden semantic concepts for images, which are distributed in multiple semantic layers. In experiment, an MSR is built based on the real retrieval from 10 different users, which can precisely describe the hidden concepts underlying images and help to bridge the gap between high-level concepts and low-level features and thus improve the retrieval performance significantly.