Augmenting metadata of digital objects
First Claim
1. A computer-implemented method comprising:
- accessing a set of videos;
determining degrees of similarity between pairs of the videos;
for each video of a plurality of the videos, training a classifier for the video, the training comprising;
forming, for the video, a training set comprising other ones of the videos based at least in part on the degrees of similarity; and
training the classifier for the video based at least in part on audiovisual features extracted from the videos in the training set;
applying the trained classifier for a first one of the videos to a second one of the videos to determine a degree of similarity between the second one of the videos and the first one of the videos; and
responsive to the degree of similarity determined by applying the trained classifier being above a threshold value;
based on the degree of similarity determined by applying the trained classifier, reducing cluster weights derived from user-supplied textual metadata of the first one of the videos, thereby obtaining first reduced cluster weight metadata; and
associating the first reduced cluster weight metadata as metadata of the second one of the videos.
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Abstract
A metadata augmentation system determines similarities between digital objects, such as digital videos, that may or may not have metadata associated with them. Based on the determined similarities, the metadata augmentation system augments metadata of objects, such as augmenting metadata of objects lacking a sufficient amount of metadata with metadata from other objects having a sufficient amount of metadata.
In one embodiment, the similarities are used to determine training sets for training of classifiers that output degrees of more specific similarities between the corresponding video and an arbitrary second video. These classifiers are then applied to add metadata from one video to another based on a degree of similarity between the videos, regardless of their respective locations within the object similarity graph.
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Citations
17 Claims
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1. A computer-implemented method comprising:
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accessing a set of videos; determining degrees of similarity between pairs of the videos; for each video of a plurality of the videos, training a classifier for the video, the training comprising; forming, for the video, a training set comprising other ones of the videos based at least in part on the degrees of similarity; and training the classifier for the video based at least in part on audiovisual features extracted from the videos in the training set; applying the trained classifier for a first one of the videos to a second one of the videos to determine a degree of similarity between the second one of the videos and the first one of the videos; and responsive to the degree of similarity determined by applying the trained classifier being above a threshold value; based on the degree of similarity determined by applying the trained classifier, reducing cluster weights derived from user-supplied textual metadata of the first one of the videos, thereby obtaining first reduced cluster weight metadata; and associating the first reduced cluster weight metadata as metadata of the second one of the videos. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 13)
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10. A non-transitory computer-readable storage medium having executable computer program instructions embodied therein, actions of the computer program instructions comprising:
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accessing a set of digital objects; determining degrees of similarity between pairs of the objects; and for each object of a plurality of the objects, training a classifier for the object, the training comprising; forming, for the object, a training set comprising other ones of the objects based at least in part on the degrees of similarity; and training the classifier for the object based at least in part on features extracted from the objects in the training set; applying the trained classifier for a first one of the objects to a second one of the objects to determine a degree of similarity between the second one of the objects and the first one of the objects; and responsive to the degree of similarity being above a threshold value; based on the degree of similarity, reducing cluster weights derived from user-supplied textual metadata of the first one of the objects, thereby obtaining first reduced cluster weight metadata; and associating the first reduced cluster weight metadata as metadata of the second one of the objects. - View Dependent Claims (11, 12, 14)
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15. A computer system comprising:
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a computer processor; and a computer-readable storage medium storing a computer program executable by the computer processor and performing actions comprising; accessing a set of videos; determining degrees of similarity between pairs of the videos; and for each video of a plurality of the videos, training a classifier for the video, the training comprising; forming, for the video, a training set comprising other ones of the videos based at least in part on the degrees of similarity; and training the classifier for the video based at least in part on audiovisual features extracted from the videos in the training set; applying the trained classifier for a first one of the videos to a second one of the videos to determine a degree of similarity between the second one of the videos and the first one of the videos; and responsive to the degree of similarity being above a threshold value; based on the degree of similarity, reducing cluster weights derived from user-supplied textual metadata of the first one of the videos, thereby obtaining first reduced cluster weight metadata; and associating the first reduced cluster weight metadata as metadata of the second one of the videos. - View Dependent Claims (16, 17)
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Specification