Determining feature vectors for video volumes
First Claim
1. A computer-implemented method comprising:
- accessing a feature codebook comprising a set of representative feature vectors representing at least visual properties of digital videos;
identifying, in a plurality of digital videos, a plurality of candidate volumes representing spatio-temporal portions of the digital videos, wherein each of the candidate volumes corresponds to a contiguous sequence of spatial portions of video frames having a starting time and an ending time;
associating features with each candidate volume of a plurality of the identified candidate volumes, the associating comprising;
identifying a plurality of temporal segments of the candidate volume;
for each of the identified temporal segments;
determining a feature vector from at least visual properties of the temporal segment, andassociating with the temporal segment a representative feature vector from the feature codebook that is most similar to the feature vector;
determining features for the candidate volume, the features comprising temporal relationship features comprising, for each of a plurality of the representative feature vectors of the feature codebook, quantifications of occurrences of the representative feature vector within the candidate volume with respect to occurrences of other ones of the representative feature vectors within the candidate volume, the occurrences quantified according to a temporal operator;
assigning a label to the candidate volume using the determined temporal relationship features, the label indicating a particular object or action represented by the candidate volume; and
storing the label in association with the candidate volume.
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Accused Products
Abstract
A volume identification system identifies a set of unlabeled spatio-temporal volumes within each of a set of videos, each volume representing a distinct object or action. The volume identification system further determines, for each of the videos, a set of volume-level features characterizing the volume as a whole. In one embodiment, the features are based on a codebook and describe the temporal and spatial relationships of different codebook entries of the volume. The volume identification system uses the volume-level features, in conjunction with existing labels assigned to the videos as a whole, to label with high confidence some subset of the identified volumes, e.g., by employing consistency learning or training and application of weak volume classifiers. The labeled volumes may be used for a number of applications, such as training strong volume classifiers, improving video search (including locating individual volumes), and creating composite videos based on identified volumes.
11 Citations
15 Claims
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1. A computer-implemented method comprising:
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accessing a feature codebook comprising a set of representative feature vectors representing at least visual properties of digital videos; identifying, in a plurality of digital videos, a plurality of candidate volumes representing spatio-temporal portions of the digital videos, wherein each of the candidate volumes corresponds to a contiguous sequence of spatial portions of video frames having a starting time and an ending time; associating features with each candidate volume of a plurality of the identified candidate volumes, the associating comprising; identifying a plurality of temporal segments of the candidate volume; for each of the identified temporal segments; determining a feature vector from at least visual properties of the temporal segment, and associating with the temporal segment a representative feature vector from the feature codebook that is most similar to the feature vector; determining features for the candidate volume, the features comprising temporal relationship features comprising, for each of a plurality of the representative feature vectors of the feature codebook, quantifications of occurrences of the representative feature vector within the candidate volume with respect to occurrences of other ones of the representative feature vectors within the candidate volume, the occurrences quantified according to a temporal operator; assigning a label to the candidate volume using the determined temporal relationship features, the label indicating a particular object or action represented by the candidate volume; and storing the label in association with the candidate volume. - View Dependent Claims (2, 3, 4, 5)
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6. A computer-implemented method comprising:
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identifying, in a digital video, a volume representing a spatio-temporal portion of the digital video, the volume corresponding to a contiguous sequence of spatial portions of frames of the video having a starting time and an ending time; identifying a set of temporal segments of the volume, each segment associated with a time within the video and with a spatial portion of a frame of the video that corresponds to the associated time; accessing a feature codebook comprising a set of representative feature vectors representing at least visual properties of digital videos; for each of a plurality of the temporal segments; deriving a feature vector from at least visual content of the temporal segment; associating, with the temporal segment, a feature vector from the feature codebook that is most similar to the feature vector derived from the visual content; for the volume, and for at least a first one of the representative feature vectors and a second one of the representative feature vectors; determining temporal relationship features comprising quantifications of occurrences of the first one of the representative feature vectors within the candidate volume with respect to occurrences of the second one of the representative feature vectors within the candidate volume, the occurrences quantified according to a temporal operator; and assigning a label to the volume using the determined temporal relationship features, the label indicating a particular object or action represented by the volume; and storing a degree of the temporal relationship as a feature representing the volume. - View Dependent Claims (7, 8, 9, 10, 11)
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12. A non-transitory computer-readable storage medium storing executable computer program instructions comprising:
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instructions for identifying, in a plurality of digital videos, a plurality of candidate volumes representing spatio-temporal segments of the digital videos, wherein each of the candidate volumes corresponds to a contiguous sequence of spatial portions of video frames having a starting time and an ending time, and potentially represents a discrete object or action within the video frames; instructions for forming a feature codebook based on the identified plurality of candidate volumes, the forming comprising; dividing each of a plurality of the candidate volume into temporal segments; for each segment of the determined temporal segments, determining a segment feature vector representing at least visual properties of the segment; and forming the feature codebook by clustering the segment feature vectors into a set of representative feature vectors; instructions for associating features with each candidate volume of a plurality of the identified candidate volumes, the associating comprising; dividing the candidate volume into temporal segments; for each temporal segment; determining a feature vector from at least visual properties of the temporal segment; associating with the temporal segment a representative feature vector from the feature codebook that is most similar to the feature vector; determining features for the candidate volume, the features comprising temporal relationship features determined by comparing the times of occurrence within the candidate volume of the representative feature vectors associated with the temporal segments, the time of occurrence of a representative feature vector being the time of occurrence of the temporal segment with which it is associated, wherein the temporal relationship features comprise, for each a plurality of the representative feature vectors of the feature codebook, quantifications of occurrences of the representative feature vector within the candidate volume with respect to occurrences of other ones of the representative feature vectors within the candidate volume, the occurrences quantified according to a temporal operator; assigning a label to the candidate volume using the determined temporal relationship features, the label indicating a particular object or action represented by the candidate volume to which the label is assigned; and storing the determined features in association with the candidate volume. - View Dependent Claims (13, 14, 15)
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Specification