摘要:
Probabilistic retrospective event detection is described. In one aspect, event parameters are initialized to identify a number of events from a corpus of documents. Using a generative model, documents are determined to be associated with an event to detect representative events from the identified number of events.
摘要:
Systems, engines, user interfaces, and methods allow a user to select a group of images, such as digital photographs, and assign to the group of images the name of a person who is represented in each of the images. The name is automatically propagated to the face of the person, each time the person's face occurs in an image. In one implementation, names and associations are shared between a browsing mode for viewing multiple images at once and a viewer mode, for viewing one image at a time. The browsing mode can provide a menu of candidate names for annotating a face in a single image of the viewer mode. Likewise, the viewer mode can provide annotated face information to the browser mode for facilitating name propagation. Identification of a person's face in multiple images can be accomplished not only by finding similarities in facial features but also by finding similarities in contextual features near the face in different images.
摘要:
A method and system for generating clusters of images for a search result of an image query is provided. When an original image query is received, the search system identifies text associated with the original image query by submitting the original image query to a search engine. The search system identifies phrases from the text of the web page containing the search result. The search system uses each of the identified phrases as an image query and submits the image queries to an image search engine. The search system considers the image search result for each image query to represent a cluster of related images. The search system then presents the clusters of images as the images of the image search result of the original image query.
摘要:
A method and system for generating clusters of images for a search result of an image query is provided. When an original image query is received, the search system identifies text associated with the original image query by submitting the original image query to a search engine. The search system identifies phrases from the text of the web page containing the search result. The search system uses each of the identified phrases as an image query and submits the image queries to an image search engine. The search system considers the image search result for each image query to represent a cluster of related images. The search system then presents the clusters of images as the images of the image search result of the original image query.
摘要:
Systems, engines, user interfaces, and methods allow a user to select a group of images, such as digital photographs, and assign to the group of images the name of a person who is represented in each of the images. The name is automatically propagated to the face of the person, each time the person's face occurs in an image. In one implementation, names and associations are shared between a browsing mode for viewing multiple images at once and a viewer mode, for viewing one image at a time. The browsing mode can provide a menu of candidate names for annotating a face in a single image of the viewer mode. Likewise, the viewer mode can provide annotated face information to the browser mode for facilitating name propagation. Identification of a person's face in multiple images can be accomplished not only by finding similarities in facial features but also by finding similarities in contextual features near the face in different images.
摘要:
Systems, engines, user interfaces, and methods allow a user to select a group of images, such as digital photographs, and assign to the group of images the name of a person who is represented in each of the images. The name is automatically propagated to the face of the person, each time the person's face occurs in an image. In one implementation, names and associations are shared between a browsing mode for viewing multiple images at once and a viewer mode, for viewing one image at a time. The browsing mode can provide a menu of candidate names for annotating a face in a single image of the viewer mode. Likewise, the viewer mode can provide annotated face information to the browser mode for facilitating name propagation. Identification of a person's face in multiple images can be accomplished not only by finding similarities in facial features but also by finding similarities in contextual features near the face in different images.
摘要:
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.
摘要:
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.
摘要:
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.
摘要:
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.