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Face naming in news images via multiple instance learning and hybrid recurrent convolutional neural network

  • Annotations of subject IDs in images are very important as ground truth for face recognition applications and news retrieval systems. Face naming is becoming a significant research topic in news image indexing applications. By exploiting the uniqueness of name, face naming is transformed to the problem of multiple instance learning (MIL) with exclusive constraint, namely the eMIL problem. First, the positive bags and the negative bags are automatically annotated by a hybrid recurrent convolutional neural network and a distributed affinity propagation cluster. Next, positive instance selection and updating are used to reduce the influence of false-positive bag and to improve the performance. Finally, max exclusive density and iterative Max-ED algorithms are proposed to solve the eMIL problem. The experimental results show that the proposed algorithms achieve a significant improvement over other algorithms.

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Metadaten
Name:Rätsch, Matthias
DOI:https://doi.org/10.1117/1.JEI.27.3.033036
ISSN:1017-9909
eISSN:1560-229X
Erschienen in:Journal of electronic imaging
Publisher:SPIE ; IS & T
Place of publication:Bellingham, Wash.
Document Type:Article
Language:English
Year of Publication:2018
Tag:distributed affinity propagation cluster; face naming; hybrid recurrent convolutional neural network; iterative max-exclusive density; max exclusive density; mulitple instance learning
Volume:27
Issue:3
Pagenumber:26
First Page:1
Last Page:26
Dewey Decimal Classification:620 Ingenieurwissenschaften und Maschinenbau
Access Rights:Nein