IP Library Granted Patent US 9,665,775
Granted Patent B2
US 9,665,775 · App. 15/217,421 · Granted May 30, 2017

Identifying scene boundaries using group sparsity analysis

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Quick Facts
Patent No.
US 9,665,775
App. No.
15/217,421
Filed
Jul 22, 2016
Granted
May 30, 2017
Kind
B2
Art Unit
2668
USPC
382/197
Abstract

A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Claims (34)

1. A method for determining scene boundaries within a video sequence including a time sequence of video frames, each video frame including an array of image pixels having pixel values, comprising:

a) selecting a set of video frames from the video sequence, the set of video frames comprising a starting point and an ending point;

b) extracting a feature vector for each video frame in the set of video frames;

c) applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames in the set of video frames, each feature vector for the other video frames in the group sparse combination having an associated weighting coefficient, wherein the weighting coefficients for feature vectors corresponding to other video frames that are most similar to the particular video frame are non-zero, and the weighting coefficients for feature vectors corresponding to other video frames that are most dissimilar from the particular video frame are zero;

d) analyzing the weighting coefficients to determine a video frame cluster of temporally-contiguous, similar video frames that includes the particular video frame;

e) repeating steps c)-d) for a plurality of particular video frames to provide a plurality of video frame clusters;

f) identifying one or more scene boundaries corresponding scenes in the video sequence based on the locations of boundaries between the determined video frame clusters; and

g) storing an indication of the identified scene boundaries in a processor-accessible memory;

wherein selecting a set of video frames from the video sequence comprises temporally sub-sampling the video sequence to select a subset of video frames.

2. The method of claim 1 wherein selecting a set of video frames further comprises separating the selected subset of video frames by a predefined interval.

3. The method of claim 2 wherein the predefined interval comprises an interval defined by every tenth video frame in the video sequence.

4. The method of claim 1 wherein selecting a set of video frames from the video sequence comprises using a user interface to enable a user to manually indicate the starting point and the ending point for the set of video frames.

5. A method for determining scene boundaries within a video sequence including a time sequence of video frames, each video frame including an array of image pixels having pixel values, comprising:

a) selecting a set of video frames from the video sequence, the set of video frames comprising a starting point and an ending point;

b) extracting a feature vector for each video frame in the set of video frames;

c) applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames in the set of video frames, each feature vector for the other video frames in the group sparse combination having an associated weighting coefficient, wherein the weighting coefficients for feature vectors corresponding to other video frames that are most similar to the particular video frame are non-zero, and the weighting coefficients for feature vectors corresponding to other video frames that are most dissimilar from the particular video frame are zero;

d) analyzing the weighting coefficients to determine a video frame cluster of temporally-contiguous, similar video frames that includes the particular video frame;

e) repeating steps c)-d) for a plurality of particular video frames to provide a plurality of video frame clusters;

f) identifying one or more scene boundaries corresponding scenes in the video sequence based on the locations of boundaries between the determined video frame clusters; and

g) storing an indication of the identified scene boundaries in a processor-accessible memory;

wherein the video sequence comprises a plurality of independently encoded video frames and a plurality of video frames that are encoded using inter-frame coding, and wherein selecting a set of video frames from the video sequence comprises selecting at least a subset of the plurality of independently encoded video frames.

6. The method of claim 5 wherein selecting a set of video frames from the video sequence comprises using a user interface to enable a user to manually indicate the starting point and the ending point for the set of video frames.

7. A method for determining scene boundaries within a video sequence including a time sequence of video frames, each video frame including an array of image pixels having pixel values, comprising:

a) selecting a set of video frames from the video sequence, the set of video frames comprising a starting point and an ending point;

b) extracting a feature vector for each video frame in the set of video frames;

c) applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames in the set of video frames, each feature vector for the other video frames in the group sparse combination having an associated weighting coefficient, wherein the weighting coefficients for feature vectors corresponding to other video frames that are most similar to the particular video frame are non-zero, and the weighting coefficients for feature vectors corresponding to other video frames that are most dissimilar from the particular video frame are zero;

d) analyzing the weighting coefficients to determine a video frame cluster of temporally-contiguous, similar video frames that includes the particular video frame;

e) repeating steps c)-d) for a plurality of particular video frames to provide a plurality of video frame clusters;

f) identifying one or more scene boundaries corresponding scenes in the video sequence based on the locations of boundaries between the determined video frame clusters; and

g) storing an indication of the identified scene boundaries in a processor-accessible memory;

wherein extracting a feature vector for each video frame comprises extracting a color channel of each video frame.

8. The method of claim 7 wherein the color channel comprises a green color channel.

9. The method of claim 7 wherein the color channel comprises pixel values for a plurality of color channels.

10. The method of claim 7 wherein selecting a set of video frames from the video sequence comprises using a user interface to enable a user to manually indicate the starting point and the ending point for the set of video frames.

Assignments (8)
SHORT-FORM PATENTS SECURITY AGREEMENT Recorded Sep 5, 2025
From: KODAK ALARIS LLC
To: ENCINA PRIVATE CREDIT SPV 2, LLC, AS COLLATERAL AGENT
Reel/Frame 072818/0674 →
RELEASE OF SECURITY INTEREST Recorded Aug 29, 2025
From: FGI WORLDWIDE LLC
To: KODAK ALARIS LLC
Reel/Frame 072740/0681 →
CHANGE OF NAME Recorded Oct 31, 2024
From: KODAK ALARIS INC.
To: KODAK ALARIS LLC
Reel/Frame 069282/0866 →
RELEASE OF SECURITY INTEREST Recorded Aug 7, 2024
From: THE BOARD OF THE PENSION PROTECTION FUND
To: KODAK ALARIS INC.
Reel/Frame 068481/0300 →
SECURITY AGREEMENT Recorded Aug 2, 2024
From: KODAK ALARIS INC.
To: FGI WORLDWIDE LLC
Reel/Frame 068325/0938 →
ASSIGNMENT OF SECURITY INTEREST Recorded Nov 17, 2021
From: KPP (NO. 2) TRUSTEES LIMITED
To: THE BOARD OF THE PENSION PROTECTION FUND
Reel/Frame 058175/0651 →
SECURITY INTEREST Recorded Oct 5, 2020
From: KODAK ALARIS INC.
To: KPP (NO. 2) TRUSTEES LIMITED
Reel/Frame 053993/0454 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 24, 2016
From: KUMAR, MRITYUNJAY; LOUI, ALEXANDER C.; PILLMAN, BRUCE HAROLD
To: KODAK ALARIS INC.
Reel/Frame 039527/0978 →