Automatic relevance feedback for video retrieval

The paper presents an automatic relevance feedback method for improving retrieval accuracy in video databases. We first demonstrate a representation based on a template-frequency model (TFM) that allows the full use of the temporal dimension. We then integrate the TFM with a self-training neural network structure to capture adaptively different degrees of visual importance in a video sequence. Forward and backward signal propagation is the key in this automatic relevance feedback method in order to enhance retrieval accuracy.