Towards large scale multimedia indexing: A case study on person discovery in broadcast news

Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI:10.1145/3095713.3095732

N. Le, H. Bredin, G. Sargent, Miquel India, Paula Lopez-Otero, C. Barras, Camille Guinaudeau, G. Gravier, G. B. Fonseca, I. Freire, Zenilton K. G. Patrocínio, S. Guimarães, Gerard Martí, J. Morros, J. Hernando, Laura Docío Fernández, C. García-Mateo, S. Meignier, J. Odobez

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引用次数: 13

Abstract

The rapid growth of multimedia databases and the human interest in their peers make indices representing the location and identity of people in audio-visual documents essential for searching archives. Person discovery in the absence of prior identity knowledge requires accurate association of audio-visual cues and detected names. To this end, we present 3 different strategies to approach this problem: clustering-based naming, verification-based naming, and graph-based naming. Each of these strategies utilizes different recent advances in unsupervised face / speech representation, verification, and optimization. To have a better understanding of the approaches, this paper also provides a quantitative and qualitative comparative study of these approaches using the associated corpus of the Person Discovery challenge at MediaEval 2016. From the results of our experiments, we can observe the pros and cons of each approach, thus paving the way for future promising research directions.

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迈向大规模多媒体索引:以广播新闻中的人物发现为例

多媒体数据库的迅速发展以及人们对其同侪的兴趣，使得音像文献中代表人物位置和身份的索引成为检索档案的必要条件。在没有先前身份知识的情况下，发现人物需要将视听线索和检测到的姓名准确地联系起来。为此，我们提出了3种不同的策略来解决这个问题:基于聚类的命名、基于验证的命名和基于图的命名。这些策略中的每一种都利用了无监督面部/语音表示、验证和优化方面的最新进展。为了更好地理解这些方法，本文还使用2016年MediaEval个人发现挑战的相关语料库对这些方法进行了定量和定性的比较研究。从我们的实验结果中，我们可以观察到每种方法的优缺点，从而为未来有前途的研究方向铺平道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing

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