The paper addresses the problem of distinguishing between pornographic and non-pornographic photographs, for the design of semantic filters for the web. Both, decision forests of trees built according to CART (Classification And Regression Trees) methodology and Support Vectors Machines (SVM), have been used to perform the classification. The photographs are described by a set of low-level features, features that can be automatically computed simply on gray-level and color representation of the image. The database used in our experiments contained 1500 photographs, 750 of which labeled as pornographic on the basis of the independent judgement of several viewers.

Ciocca, G., Cusano, C., Schettini, R. (2003). On the Detection of pornographic digital images. In Visual Communications and Image Processing (pp.2105-2113).

On the Detection of pornographic digital images

CIOCCA, GIANLUIGI;CUSANO, CLAUDIO;SCHETTINI, RAIMONDO
2003

Abstract

The paper addresses the problem of distinguishing between pornographic and non-pornographic photographs, for the design of semantic filters for the web. Both, decision forests of trees built according to CART (Classification And Regression Trees) methodology and Support Vectors Machines (SVM), have been used to perform the classification. The photographs are described by a set of low-level features, features that can be automatically computed simply on gray-level and color representation of the image. The database used in our experiments contained 1500 photographs, 750 of which labeled as pornographic on the basis of the independent judgement of several viewers.
image classification, image filtering, pornographic image detection
English
Visual Communications and Image Processing
2003
Ciocca, G., Cusano, C., Schettini, R. (2003). On the Detection of pornographic digital images. In Visual Communications and Image Processing (pp.2105-2113).
Ciocca, G; Cusano, C; Schettini, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/23858
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