Method and apparatus for multi-dimensional content search and video identification
First Claim
1. A method of organization of a multi-dimensional video database using a robust hash of a multi-dimensional vector signature as a traversal index, the method comprising:
- generation of a robust hash value as a traversal index from multiple parameters extracted from a region of interest in a frame of a video sequence; and
storing data associated with the video sequence at a leaf node addressed by the robust hash value, wherein the leaf node is a member of a plurality of leaf nodes in a multi-dimensional video database.
14 Assignments
1 Petition
Accused Products
Abstract
A multi-dimensional database and indexes and operations on the multi-dimensional database are described which include video search applications or other similar sequence or structure searches. Traversal indexes utilize highly discriminative information about images and video sequences or about object shapes. Global and local signatures around keypoints are used for compact and robust retrieval and discriminative information content of images or video sequences of interest. For other objects or structures relevant signature of pattern or structure are used for traversal indexes. Traversal indexes are stored in leaf nodes along with distance measures and occurrence of similar images in the database. During a sequence query, correlation scores are calculated for single frame, for frame sequence, and video clips, or for other objects or structures.
150 Citations
27 Claims
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1. A method of organization of a multi-dimensional video database using a robust hash of a multi-dimensional vector signature as a traversal index, the method comprising:
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generation of a robust hash value as a traversal index from multiple parameters extracted from a region of interest in a frame of a video sequence; and storing data associated with the video sequence at a leaf node addressed by the robust hash value, wherein the leaf node is a member of a plurality of leaf nodes in a multi-dimensional video database. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A method to post process signatures and associated data to evaluate a video sequence of interest and stored video sequences to increase accuracy and confidence of a video sequence match, the method comprising:
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calculating a distance between signatures of a query and signatures of features of a video database entry including signatures for a region around a keypoint to identify candidate matching frames; correlating changes in signatures between the query and the video database entry for the candidate matching frames to provide a factor in a sequence correlation score to identify likely matching frames; and providing a sequence correlation in time using differences in frame numbers between pairs of the likely matching frames to identify a likely matching video sequence. - View Dependent Claims (21, 22)
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23. A method of generating a likelihood score for matching frames of a query video and an original video, the method comprising:
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generating a similarity measure between a query video and an original video based on individual frame similarity scores that exceed a threshold score, wherein the original video is an entry in a video database; generating a time correlation using relative differences in frame numbers of the original video and the query video; and generating a correlation score between the original video and the query video by using a combination of the similarity measure and the time correlation. - View Dependent Claims (25)
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24. The method of 23 where the time correlation uses a relative time rate between the original video and the query video as determined by a first few matching signatures between the original video and the query video.
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26. A method to convert documents or activity such as online user session information or any natural event or activity into multi-dimensional vectors, the method comprising:
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classifying documents, events, activity for learning by inference by a multi-dimensional vector; and expecting certain behavior or next state in an activity, wherein the expected next state or the certain behavior is generated by a decision tree or a rule based system that takes as input one or more identified documents or classifications.
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27. The method of 26 wherein identification of a video or object is used along with other user and online website parameters to predict a user state or a user activity interest.
Specification