Learning concepts for video annotation

A non-transitory computer-readable storage medium having executable computer program instructions embodied therein for learning concepts applicable to videos, actions of the computer program instructions comprising:

• storing a set of concepts derived from textual metadata of a plurality of videos;
• initializing a set of candidate classifiers, each candidate classifier associated with one of the concepts;
• extracting features from the plurality of videos, including a set of training features from a training set of the videos and a set of validation features from a validation set of the videos;
• learning accurate classifiers for the concepts by iteratively performing the steps of:
• training the candidate classifiers based at least in part on the set of training features;
• determining which of the trained candidate classifiers accurately classify videos, based at least in part on application of the trained candidate classifiers to the set of validation features;
• applying the candidate classifiers determined to be accurate to ones of the features, thereby obtaining a set of scores, and
• adding the set of scores to the set of training features;
• and
• storing the candidate classifiers determined to be accurate.

A computer system for learning concepts applicable to videos, the system comprising:

• a computer processor;
• and
• a computer program executable by the computer processor and performing actions comprising:
• storing a set of concepts derived from textual metadata of a plurality of videos;
• initializing a set of candidate classifiers, each candidate classifier associated with one of the concepts;
• extracting features from the plurality of videos, including a set of training features from a training set of the videos and a set of validation features from a validation set of the videos;
• learning accurate classifiers for the concepts by iteratively performing the steps of:
• training the candidate classifiers based at least in part on the set of training features;
• determining which of the trained candidate classifiers accurately classify videos, based at least in part on application of the trained candidate classifiers to the set of validation features;
• applying the candidate classifiers determined to be accurate to ones of the features, thereby obtaining a set of scores, and
• adding the set of scores to the set of training features;
• and
• storing the candidate classifiers determined to be accurate.

A computer-implemented method for learning concepts applicable to videos, the method comprising:

• extracting a set of concepts from textual metadata of a plurality of videos;
• initializing a set of candidate classifiers, each candidate classifier associated with one of the concepts;
• extracting a feature vector from each of the plurality of videos, including a set of training feature vectors from a training set of the videos and a set of validation feature vectors from a validation set of the videos;
• learning accurate classifiers for the concepts by iteratively performing the steps of:
• training the candidate classifiers based at least in part on the validation feature vectors;
• determining which of the trained candidate classifiers accurately classify videos and which of the trained candidate classifiers do not accurately classify videos, based at least in part on application of the trained candidate classifiers to the validation feature vectors;
• applying the candidate classifiers determined to be accurate to ones of the feature vectors, thereby obtaining a set of scores for each of the ones of the feature vectors;
• for each of the ones of the feature vectors, adding the corresponding set of scores to the feature vector, thereby obtaining an augmented feature vector;
• and
• for one of the candidate classifiers determined not to accurately classify videos:
• retraining the candidate classifier in a later iteration based at least in part on ones of the augmented feature vectors, and
• determining that the retrained candidate classifier accurately classifies videos;
• storing the candidate classifiers determined to be accurate in association with their associated concepts;
• and
• storing the augmented feature vectors in association with the videos from which they were originally extracted.