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Proceedings Paper

Unsupervised competitive neural networks for images clustering in video sequences
Author(s): Ernesto Chiarantoni; Vincenzo Di Lecce; Andrea Guerriero
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Paper Abstract

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 shots 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.

Paper Details

Date Published: 1 April 1998
PDF: 11 pages
Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); doi: 10.1117/12.304654
Show Author Affiliations
Ernesto Chiarantoni, Politecnico di Bari (Italy)
Vincenzo Di Lecce, Politecnico di Bari (Italy)
Andrea Guerriero, Politecnico di Bari (Italy)

Published in SPIE Proceedings Vol. 3307:
Applications of Artificial Neural Networks in Image Processing III
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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