The interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. To allow the search of image-based documents in digital libraries, we propose to design classifiers able to annotate images with keywords. First, we propose an image representation appropriate for scene description. Images are segmented into regions, and then indexed according to the presence of given region types. Second, we propound a classification scheme designed to separate images in the descriptor space. This is achieved by combining feature selection and kernel-method-based classification.

Image Recognition for Digital Libraries

Amato G
2004

Abstract

The interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. To allow the search of image-based documents in digital libraries, we propose to design classifiers able to annotate images with keywords. First, we propose an image representation appropriate for scene description. Images are segmented into regions, and then indexed according to the presence of given region types. Second, we propound a classification scheme designed to separate images in the descriptor space. This is achieved by combining feature selection and kernel-method-based classification.
2004
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Scene analysis
Image segmentation
Clustering
Feature selection
Image classification
Kernel-method
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/152210
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