Unsupervised competitive neural networks for images clustering in video sequences

Automated annotation and analysis of video sequences requires efficient methods to abstract video information. The identification of shots in video sequences is an important step for summarizing the content of the video. In general, video shots need to be clustered to form more semantically significant units, such as scenes and sequences. In this paper, we describe a neural network based technique for automatic clustering of video frame signatures. The proposed technique utilizes Self Organizing Map (SOM) and/or Parallel Collision Control Network (PCC) to automatically produce a set of prototype vectors useful in the following process of scene segmentation. Results presented in this paper show that the SOM network perform efficiently, operating without requiring "a priori" knowledge about the number of shot present in the video. When we require the segmentation of a video composed by similar shots, the PCC network is suitable for its capability to preserve the acquired information.