A Multi-stage Approach for Anchor Shot Detection (original) (raw)

Abstract

In this paper we present a novel algorithm for anchor shot detection (ASD). ASD is a fundamental step for segmenting news video into stories that is among key issues for achieving efficient treatment of news-based digital libraries.

The proposed algorithm creates a set of audio/video templates of anchorperson shots in an unsupervised way, then classifies shots by comparing them to the templates. Audio similarity is evaluated by means of a new index and helps to achieve better performance than a pure video approach. The method has been tested on a wide database and compared with other state-of-the-art algorithms, demonstrating its effectiveness with respect to them.

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Authors and Affiliations

1. Dip. di Ingegneria dell’Informazione ed Ingegneria Elettrica, Università degli Studi di Salerno, Via Ponte Don Melillo, I, I-84084, Fisciano (SA), Italy
   L. D’Anna, G. Marrazzo, G. Percannella & M. Vento
2. Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli “Federico II”, Via Claudio 21, I-80125, Napoli, Italy
   C. Sansone

Authors

1. L. D’Anna
2. G. Marrazzo
3. G. Percannella
4. C. Sansone
5. M. Vento

Editor information

Editors and Affiliations

1. Hong Kong University of Science and Technology,
   Dit-Yan Yeung
2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
   James T. Kwok
3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
   Ana Fred
4. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
   Fabio Roli
5. Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
   Dick de Ridder

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D’Anna, L., Marrazzo, G., Percannella, G., Sansone, C., Vento, M. (2006). A Multi-stage Approach for Anchor Shot Detection. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_85

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• DOI: https://doi.org/10.1007/11815921\_85
• Publisher Name: Springer, Berlin, Heidelberg
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