Image cropping is a technique that is used to select the most relevant areas of an image and discarding the useless parts. Handmade selection, especially in case of large photo collections, is a time consuming task. Automatic image cropping techniques may help users, suggesting to them which part of the image is the most relevant, according to specific criteria. In this paper we suppose that the most visually salient areas of a photo are also the most relevant ones to the users. We compare three different saliency detection methods within an automatic image cropping system, to study the effectiveness of the related saliency maps for this task. We furthermore extended one of the three methods (our previous work), which is based on the extraction of keypoints from the image. Tests have been conducted onto an online available dataset, made of 5000 images which have been manually labeled by 9 users.

Saliency Based Image Cropping

BRUNO, Alessandro;
2013-01-01

Abstract

Image cropping is a technique that is used to select the most relevant areas of an image and discarding the useless parts. Handmade selection, especially in case of large photo collections, is a time consuming task. Automatic image cropping techniques may help users, suggesting to them which part of the image is the most relevant, according to specific criteria. In this paper we suppose that the most visually salient areas of a photo are also the most relevant ones to the users. We compare three different saliency detection methods within an automatic image cropping system, to study the effectiveness of the related saliency maps for this task. We furthermore extended one of the three methods (our previous work), which is based on the extraction of keypoints from the image. Tests have been conducted onto an online available dataset, made of 5000 images which have been manually labeled by 9 users.
2013
978-3-642-41180-9
Image Cropping
Visual Saliency
Visual Perception
Saliency Map
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/74324
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