Classification of images as advertisement images or non-advertisement images
First Claim
1. A method in a computing device for generating a classifier to identify advertisement images of display pages, the method comprising:
- providing training display pages, each training display page having one or more training images referenced by the training display page;
labeling each training image as being an advertisement image or a non-advertisement image;
generating a feature vector for each of the training images, the feature vector including a visual layout feature derived from the display page that references the training mage and a content feature derived from content of the training image; and
training a classifier using the feature vectors and labels of the training images to classify a target image of a target display page as an advertisement image or a non-advertisement image based on a feature vector that includes the visual layout feature derived from the target display page and the content feature derived from content of the target image.
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Abstract
An advertisement image classification system trains a binary classifier to classify images as advertisement images or non-advertisement images and then uses the binary classifier to classify images of web pages as advertisement images or non-advertisement images. During a training phase, the classification system generates training data of feature vectors representing the images and labels indicating whether an image is an advertisement image or a non-advertisement image. The classification system trains a binary classifier to classify images using training data. During a classification phase, the classification system inputs a web page with an image and generates a feature vector for the image. The classification system then applies the trained binary classifier to the feature vector to generate a score indicating whether the image is an advertisement image or a non-advertisement image.
24 Citations
20 Claims
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1. A method in a computing device for generating a classifier to identify advertisement images of display pages, the method comprising:
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providing training display pages, each training display page having one or more training images referenced by the training display page; labeling each training image as being an advertisement image or a non-advertisement image; generating a feature vector for each of the training images, the feature vector including a visual layout feature derived from the display page that references the training mage and a content feature derived from content of the training image; and training a classifier using the feature vectors and labels of the training images to classify a target image of a target display page as an advertisement image or a non-advertisement image based on a feature vector that includes the visual layout feature derived from the target display page and the content feature derived from content of the target image. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-readable storage device for classifying a target image of a target display page as an advertisement image or non-advertisement image, by a method comprising:
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providing a classifier to classify an image of a display page as an advertisement image or a non-advertisement image, the classifier having been trained using features derived from training images of training display pages and labels of the training images, the label of a training image indicating whether the training image is an advertisement image or a non-advertisement image; identifying the features of the target image of the target display page; and applying the provided classifier to the identified features of the target image to classify the target image as an advertisement image or a non-advertisement image. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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17. A computing device for identifying features of images of web pages for use in classifying images as advertisement images or non-advertisement images, comprising:
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a training data store including feature vectors for images of web pages, the images being labeled as advertisement images or non-advertisement images, the feature vectors including candidate features; a memory storing computer-executable instructions of; a component that trains a first classifier using the feature vectors with candidate features and labels of the training data store; and a component that selects as features for use in classifying images those candidate features whose weights indicate they are effective at distinguishing advertisement images from non-advertisement images; and a processor for executing the computer-executable instructions stored in the memory. - View Dependent Claims (18, 19, 20)
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Specification