Method and Apparatus for Multi-Dimensional Content Search and Video Identification
First Claim
1. A method of organization of a multi-dimensional video using a compact hash of multi-dimensional vector signature as a traversal index, the method comprising;
- generation of a robust hash as a traversal index from multiple parameters extracted from a region of interest in a frame or from a frame of a video sequence; and
storing multiple associated data or signatures at a leaf node of a multi-dimensional video database.
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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.
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Citations
27 Claims
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1. A method of organization of a multi-dimensional video using a compact hash of multi-dimensional vector signature as a traversal index, the method comprising;
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generation of a robust hash as a traversal index from multiple parameters extracted from a region of interest in a frame or from a frame of a video sequence; and storing multiple associated data or signatures at a leaf node of 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 between a video sequence of interest or an object region associated with a query object or a video sequence to increase accuracy and confidence of a video sequence match, the method comprising:
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calculating the distance between the signatures of the query and original video features including a region around a keypoint, or an object or a frame; correlating changes in signatures between a query and a database entry for a matching frame, object, or structure to provide a factor in a sequence correlation score; and providing a sequence correlation in time using differences in frame numbers between pairs of matching query and original video signatures. - View Dependent Claims (21, 22)
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23. A method of generating a likelihood score for a pair of query frames or regions and correlating between matching frames of the query and original video, the method comprising:
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generating a correlation score based on an individual frame similarity score; generating a time correlation using relative differences in frame numbers of the original video and the query video; and generating a correlation between the original video and the query video by using a change in signatures of each sequence of frames in the query video and in the original video, wherein the original video is an entry in a video database. - View Dependent Claims (25)
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24. The method of 23 where time correlation uses the relative time rate between the original and query as determined by the first few matching signatures between the original and 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