A novel cross-media layered semantic mining model

This paper presents a cross-media semantic mining model (CSMM) based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are trained and constructed with STTS (Semantic Template Training System), which are taken as the bridge to realize the transition from various low-level media feature to object semantic. Furthermore, we put forward a kind of double layers metadata structure to efficaciously store and manage mined low-level feature and high-level semantic. This model has broad application in lots of domains such as intelligent retrieval engine, medical diagnoses, multimedia design and so on. Biography: ZENG Cheng(1978– ), male, Lecturer, Ph.D., research direction: cross-media retrieval, pattern recognition.