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Soft Computing

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01.06.2011 | Focus

Automatic localization and annotation of facial features using machine learning techniques

verfasst von: Paul C. Conilione, Dianhui Wang

Erschienen in: Soft Computing | Ausgabe 6/2011

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Abstract

Content-based image retrieval (CBIR) systems traditionally find images within a database that are similar to query image using low level features, such as colour histograms. However, this requires a user to provide an image to the system. It is easier for a user to query the CBIR system using search terms which requires the image content to be described by semantic labels. However, finding a relationship between the image features and semantic labels is a challenging problem to solve. This paper aims to discover semantic labels for facial features for use in a face image retrieval system. Face image retrieval traditionally uses global face-image information to determine similarity between images. However little has been done in the field of face image retrieval to use local face-features and semantic labelling. Our work aims to develop a clustering method for the discovery of semantic labels of face-features. We also present a machine learning based face-feature localization mechanism which we show has promise in providing accurate localization.

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The ffs and d notation is not included in the diagram for clarity.

Known as Stratified K-fold cross validation.

Detailed in Sect. 2.2.1.

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Titel: Automatic localization and annotation of facial features using machine learning techniques
verfasst von: Paul C. Conilione
Dianhui Wang
Publikationsdatum: 01.06.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 6/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0586-y

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