Method for detecting scene boundaries in genre independent videos
First Claim
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1. A computer implemented method for detecting scene boundaries in videos, comprising the steps of:
- extracting audio features from an audio signal of the videos, wherein the audio features are Mel-frequency cepstral coefficients, and the audio signals are classified into semantic classes, and wherein each feature vector includes variable x1, x2, x3 indicating a number of the audio classes labels within a time window of duration [t−
WL, t], where WL is about fourteen seconds, and variables x4, x5, x6 indicating a number of the audio classes in a window of duration and variables x7, x8, x9 indicate a number of audio classes within a window [t, t+WL], and variables x10, x11 are a Bhattacharyya shape and a Mahalanobis distance between the MFCC coefficients for the window [t−
WL, t] and window [t, t+WL], respectively, and variable x12 is twice an average number of shot boundaries present in the video within a window [t−
WL, t+WL];
extracting visual features from frames of the videos;
combining the audio and visual features into the feature vectors,extracting the feature vectors from videos of different genres;
classifying the feature vectors as scene boundaries using a support vector machine, and in which the support vector machine is trained to be independent of the different genre of the videos; and
segmenting the videos according to the scene boundaries, wherein the steps are performed in a computer.
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Abstract
A computer implemented method detects scene boundaries in videos by first extracting feature vectors from videos of different genres. The feature vectors are then classified as scene boundaries using a support vector machine. The support vector machine is trained to be independent of the different genres of the videos.
16 Citations
10 Claims
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1. A computer implemented method for detecting scene boundaries in videos, comprising the steps of:
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extracting audio features from an audio signal of the videos, wherein the audio features are Mel-frequency cepstral coefficients, and the audio signals are classified into semantic classes, and wherein each feature vector includes variable x1, x2, x3 indicating a number of the audio classes labels within a time window of duration [t−
WL, t], where WL is about fourteen seconds, and variables x4, x5, x6 indicating a number of the audio classes in a window of durationand variables x7, x8, x9 indicate a number of audio classes within a window [t, t+WL], and variables x10, x11 are a Bhattacharyya shape and a Mahalanobis distance between the MFCC coefficients for the window [t−
WL, t] and window [t, t+WL], respectively, and variable x12 is twice an average number of shot boundaries present in the video within a window [t−
WL, t+WL];extracting visual features from frames of the videos; combining the audio and visual features into the feature vectors, extracting the feature vectors from videos of different genres; classifying the feature vectors as scene boundaries using a support vector machine, and in which the support vector machine is trained to be independent of the different genre of the videos; and segmenting the videos according to the scene boundaries, wherein the steps are performed in a computer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
and the Mahalanobis distance is where covariance matrices Ci and Cj, and means μ
i and μ
j represent diagonal covariances and means of the MFCC vectors before and after the time t.
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9. The method of claim 1, further comprising:
transforming the feature vectors to a higher dimensional feature space using a kernel function.
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10. The method of claim 9, in which the kernel function is a radial basis kernel.
Specification
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Current AssigneeMitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
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Original AssigneeMitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
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InventorsOtsuka, Isao, Goela, Naveen, Niu, Feng, Divakaran, Ajay, Wilson, Kevin W.
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Primary Examiner(s)Strege; John B
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Application NumberUS11/674,750Publication NumberTime in Patent Office1,245 DaysField of Search382/190, 382/199, 382/224, 382/225, 382/226, 382/227, 382/228, 382/236, 348/135, 348/699, 348/700, 345/728, 715/723, 725/45US Class Current382/190CPC Class CodesG06N 20/00 Machine learningG06N 20/10 using kernel methods, e.g. ...G06T 7/20 Analysis of motion motion e...
