Relevance feedback for content-based retrieval in video databases: a neural network approach

A neural network scheme is presented for adaptive video indexing and retrieval. First, a limited but characteristic amount of frames are extracted from each video scene, able to provide an efficient representation of the video content. For this reason, a cross correlation criterion is mini-cited using a genetic algorithm. Low level features are extracted to indicate the frame characteristics, such as color and motion segments. After the key frame extraction, the video queries are implemented directly on this small number of frames. To reduce, however, the limitation of low-level features, the human is considered as a part of the process, meaning that he/she is able to assign a degree of appropriateness for each retrieved image of the system and then restart the searching. A feedforward neural network structure is proposed as a parametric distance for the retrieval, mainly due to the highly nonlinear capabilities. An adaptation mechanism is also proposed for updating the network weights, each time a new image selection is performed by the user