Unsupervised neural network approach for efficient video description

MPEG-4 object oriented video codec implementations are rapidly emerging as a solution to compress audio-video information in an efficient way, suitable for narrowband applications. A different view is proposed in this paper: several images in a video sequence result very close to each other. Each image of the sequence can be seen as a vector in a hyperspace and the whole video can be considered as a curve described by the image-vector at a given time instant. The curve can be sampled to represent the whole video, and its evolution along the video space can be reconstructed from its video-samples. Any image in the hyperspace can be obtained by means of a reconstruction algorithm, in analogy with the reconstruction of an analog signal from its samples; anyway, here the multi-dimensional nature of the problem asks for the knowledge of the position in the space and a suitable interpolating kernel function. The definition of an appropriate Video Key-frames Codebook is introduced to simplify video reproduction; a good quality of the predicted image of the sequence might be obtained with a few information parameters. Once created and stored the VKC, the generic image in the video sequence can be referred to the selected key-frames in the codebook and reconstructed in the hyperspace from its samples. Focus of this paper is on the analysis phase of a give video sequence. Preliminary results seem promising.