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Classifying images on the web automatically

Classifying images on the web automatically,10.1117/1.1502259,Journal of Electronic Imaging,Rainer Lienhart,Alexander Hartmann

Classifying images on the web automatically   (Citations: 25)
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Numerous research works about the extraction of low-level features from images and videos have been published. However, only recently the focus has shifted to exploiting low-level features to classify images and videos automatically into semanti- cally broad and meaningful categories. In this paper, novel classification algorithms are presented for three broad and general- purpose categories. In detail, we present algorithms for distinguishing photo-like images from graphical images, actual photos from only photo-like, but artificial images and presentation slides/scientific posters from comics. On a large image database, our classification algorithm achieved an accuracy of 97.69% in separating photo-like images from graphical images. In the subset of photo-like images, true photos could be separated from ray-traced/rendered image with an accuracy of 97.3%, while with an accuracy of 99.5% the subset of graphical images was successfully partitioned into presentation slides/scientific posers and comics.
Journal: Journal of Electronic Imaging - J ELECTRON IMAGING , vol. 11, no. 4, pp. 445-454, 2002
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    • ...In [3] web images were classified into photo-like images, presentation slides, scientific posters and comics...

    Divna Djordjevicet al. Graphics Classification for Enterprise Knowledge Management

    • ...Automated classifiers to determine whether an image is a cartoon also exist [26] and may be useful here to scan the web for such images...

    Rich Gossweileret al. What's up CAPTCHA?: a CAPTCHA based on image orientation

    • ...Lienhart and Hartmann [9] propose an image classification approach based on the AdaBoost learning algorithm...

    Wenbin Shaoet al. Automatic image annotation for semantic image retrieval

    • ...However, content-based image retrieval (CBIR) is still in its infancy and most existing CBIR systems can only support feature-based image retrieval [1-6]...
    • ... address the second issue for automatic image annotation, two approaches are widely used to train the image classifiers: (a) Model-based approach by using Gaussian mixture models to approximate the underlying distributions of image classes in the high-dimensional feature space [25-27]; (b) SVM-based approach by using support vector machines (SVM) to directly learn the maximum margins between the positive images and the negative images [ ...
    • ...On the other hand, SVM-based approach is able to enable more effective classifier training with small generalization error rate in high-dimensional feature space [6,35,40]...

    Yuli Gaoet al. Automatic image annotation by incorporating feature hierarchy and boos...

    • ...When large-scale image collections come into view, there is an urgent need to support automatic image annotation based on their contents so that semantic image retrieval via keywords can be achieved [1-6]...
    • ...To address the first issue, the underlying visual patterns that are used for image content representation and feature extraction should be able to characterize the middle-level image semantics effectively and efficiently [1-6]...

    Yuli Gaoet al. Incorporating concept ontology to enable probabilistic concept reasoni...

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