CN109635747A - The automatic abstracting method of video cover and device - Google Patents

The automatic abstracting method of video cover and device Download PDF

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Publication number
CN109635747A
CN109635747A CN201811532062.1A CN201811532062A CN109635747A CN 109635747 A CN109635747 A CN 109635747A CN 201811532062 A CN201811532062 A CN 201811532062A CN 109635747 A CN109635747 A CN 109635747A
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Prior art keywords
frame
characteristic point
video
rank
module
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CN201811532062.1A
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Chinese (zh)
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张勇
朱立松
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CCTV INTERNATIONAL NETWORKS WUXI Co Ltd
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CCTV INTERNATIONAL NETWORKS WUXI Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • G06V20/47—Detecting features for summarising video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering

Abstract

The invention discloses a kind of automatic abstracting method of video cover and devices, wherein includes: that S10 unzips it video to obtain sequence frame in the automatic abstracting method of video cover；S20 successively extracts the local feature region of each frame, and calculates the intensity of each characteristic point；S30 is directed to every frame, retains the stronger characteristic point of preset quantity intensity；S40 carries out the quantization of K rank to the characteristic point of reservation；S50 is directed to every frame, counts the quantity of every rank characteristic point in K rank characteristic point, obtains the feature vector of each frame；S60 carries out Kmeans cluster to the feature vector of all frames, and will assemble the most one kind of quantity as the home court scape of video；S70, which is extracted, corresponds to the smallest frame of class center point apart from home court scape, and as the surface plot of video, realize automatic, the accurate extraction of video surface plot, and other videos that the surface plot extracted can embody the content of video to the greatest extent, be different from same type.

Description

The automatic abstracting method of video cover and device
Technical field
The present invention relates to technical field of data processing more particularly to a kind of automatic abstracting methods of video cover and device.
Background technique
When video is placed on webpage or cell phone application (application) on be shown when, it usually needs provide one for video Surface plot.In order to provide the user with more information as far as possible, the selection of surface plot generally requires two requirements of satisfaction: as far as possible The content of video can be embodied and be different from the surface plot of other videos of same type as far as possible.
When artistic level is more demanding, such as film poster, surface plot usually by manually carrying out artistic creation, obtain compared with For exquisite poster；When artistic level requires lower, such as network video, surface plot from video usually by manually directly extracting One frame image obtains, and this method high labor cost, speed are slow and can not carry out extensive automatic processing.
Summary of the invention
In view of the above shortcomings of the prior art, the present invention provides a kind of automatic abstracting method of video cover and device, have Effect solves the technical issues of cannot extracting the surface plot for best embodying video content automatically in the prior art.
To achieve the goals above, the invention is realized by the following technical scheme:
A kind of automatic abstracting method of video cover, comprising:
S10 unzips it video to obtain sequence frame；
S20 successively extracts the local feature region of each frame, and calculates the intensity of each characteristic point；
S30 is directed to every frame, retains the stronger characteristic point of preset quantity intensity；
S40 carries out the quantization of K rank to the characteristic point of reservation；
S50 is directed to every frame, counts the quantity of every rank characteristic point in K rank characteristic point, obtains the feature vector of each frame；
S60 carries out Kmeans cluster to the feature vector of all frames, and will assemble the most one kind of quantity as video Home court scape；
S70, which is extracted, corresponds to the smallest frame of class center point apart from home court scape, and as the surface plot of video.
It is further preferred that after step slo further include:
S11 is extracted according to the sequence frame that preset rules obtain decompression, obtains the set of frame；
In step S20, the local feature region of each frame in set is successively extracted.
It is further preferred that in step S20, specifically: the SIFT feature or SURF characteristic point of each frame are successively extracted, And calculate the intensity of each characteristic point.
It is further preferred that in step s 40, further comprising:
S41 carries out Kmeans cluster to the characteristic point of reservation；
S42 quantifies characteristic point according to the central point of all categories that cluster obtains.
It is further preferred that in step s 40, specifically: it is carried out according to characteristic point of the preset K rank characteristic point to reservation The quantization of K rank.
The present invention also provides a kind of automatic draw-out devices of video cover, comprising:
Video decompression module obtains sequence frame for unziping it to video；
Characteristic extracting module for successively extracting the local feature region for each frame that video decompression module obtains, and calculates every The intensity of a characteristic point；
Characteristic point reservation module, according to the calculated result of characteristic extracting module, for every frame, retain preset quantity intensity compared with Strong characteristic point；
Quantization modules, the characteristic point for retaining characteristic point reservation module carry out the quantization of K rank；
Statistical module counts every rank feature in K rank characteristic point for every frame for the quantized result according to quantization modules The quantity of point, obtains the feature vector of each frame；
Cluster module, the feature vector of all frames for obtaining to statistical module counts carry out Kmeans cluster, and will Assemble home court scape of the most one kind of quantity as video；
Surface plot extraction module corresponds to the smallest frame of class center point apart from home court scape for extracting, and as The surface plot of video.
It is further preferred that in the automatic draw-out device of video cover further include:
Frame abstraction module, the sequence frame for being obtained according to preset rules to decompression extract, and obtain the set of frame；
In characteristic extracting module, the local feature region of each frame in the set that frame abstraction module extracts successively is extracted, and is counted Calculate the intensity of each characteristic point.
It is further preferred that the SIFT feature or SURF characteristic point of each frame are successively extracted in characteristic extracting module, and Calculate the intensity of each characteristic point.
It is further preferred that in quantization modules, comprising:
Cluster cell, for carrying out Kmeans cluster to the characteristic point retained characteristic point reservation module；
Quantifying unit, the central point of all categories for being clustered according to cluster cell quantify characteristic point.
It is further preferred that carrying out K rank amount according to characteristic point of the preset K rank characteristic point to reservation in quantization modules Change.
In the automatic abstracting method of video cover provided by the invention and device, to the part of video compression, each frame of extraction Feature simultaneously remains in each frame after the characteristic point of the stronger preset quantity of intensity, K rank quantization is carried out to it, and then obtain each frame The feature vector of quantization clusters quantization characteristic vector and takes the home court for assembling the most classification of sample number as video Scape extracts and corresponds to surface plot of the smallest frame of class center point as video apart from home court scape, realizes oneself of video surface plot Other views that dynamic, the accurate surface plot for extracting, and extracting can embody the content of video to the greatest extent, be different from same type Frequently.
Detailed description of the invention
In conjunction with attached drawing, and by reference to following detailed description, it will more easily have more complete understanding to the present invention And its adjoint advantage and feature is more easily to understand, in which:
Fig. 1 is the automatic abstracting method flow diagram of video cover in the present invention；
Fig. 2 is SIFT feature extraction process schematic diagram in the present invention；
Fig. 3 is the automatic draw-out device structural schematic diagram of video cover in the present invention.
The automatic draw-out device of 100- video cover, 110- video decompression module, 120- characteristic extracting module, 130- characteristic point Reservation module, 140- quantization modules, 150- statistical module, 160- cluster module, 170- surface plot extraction module.
Specific embodiment
To keep the contents of the present invention more clear and easy to understand, below in conjunction with Figure of description, the contents of the present invention are made into one Walk explanation.Certainly the invention is not limited to the specific embodiment, general replacement known to those skilled in the art It is included within the scope of protection of the present invention.
It is as shown in Figure 1 the automatic abstracting method flow diagram of video cover provided by the invention, it can be seen from the figure that Include: in the automatic abstracting method of video cover
S10 unzips it video to obtain sequence frame；
S20 successively extracts the local feature region of each frame, and calculates the intensity of each characteristic point；
S30 is directed to every frame, retains the stronger characteristic point of preset quantity intensity；
S40 carries out the quantization of K rank to the characteristic point of reservation；
S50 is directed to every frame, counts the quantity of every rank characteristic point in K rank characteristic point, obtains the feature vector of each frame；
S60 carries out Kmeans cluster to the feature vector of all frames, and will assemble the most one kind of quantity as video Home court scape；
S70, which is extracted, corresponds to the smallest frame of class center point apart from home court scape, and as the surface plot of video.
In the method, after the video V for obtaining needing to extract cover page, with being unziped it to obtain frame sequence Column, V=(F1,F2,F3,...,Fn), wherein n indicates the quantity of frame in video, FnIndicate the n-th frame in video.
After obtaining frame sequence, the local feature region of each frame is successively extracted, such as extracts SIFT feature or SURF characteristic point Deng, and calculate the intensity of each characteristic point.The algorithm extracted for local feature region is not specifically limited here, be can be used and is appointed The algorithm of meaning extracts, and for extracting SIFT feature, multiple SIFT are calculated by SIFT algorithm in one frame of image Corresponding office can be calculated according to the vector of 128 dimension in characteristic point (vector description that each characteristic point is tieed up by one 128) The intensity of portion's characteristic point.As shown in Figure 2, wherein Fig. 2 (a) is an original frame image, extracts the frame image using SIFT algorithm SIFT feature and calculate the intensity of each SIFT feature, as shown in Fig. 2 (b), specifically, the great circle in figure indicate intensity compared with Strong SIFT feature, ringlet indicate the weaker SIFT feature of intensity.
It is calculated after the intensity of each characteristic point (local feature region of extraction), it is stronger to retain intensity in every frame image Characteristic point, that is, be directed to every frame image, the intensity of all characteristic points is ranked up, according to the size of preset quantity to part Characteristic point is retained.As in one example, preset quantity 100, then preceding 100 characteristic points in the sequence of keeping characteristics point intensity (sequence is by by force to weak).
After the characteristic point for remaining identical quantity for every frame image, the quantization of K rank is carried out to each characteristic point immediately.
For the method for quantization, in one embodiment, realized using the method for cluster, it is specific: first against all frames The characteristic point of reservation carries out Kmeans cluster, after obtaining K cluster, the central point of each cluster is calculated, with this according to each The central point of cluster carries out the quantization of K rank to the characteristic point that all frames retain, and the corresponding characteristic point of each classification is quantified as such The value of other central point.In one example, it is assumed that carry out the K cluster that Kmeans is clustered for the characteristic point that all frames retain Respectively p1,p2,...,pk(pkIndicate k-th of classification), the central point vector of each classification is z1,z2,...,zk(zkIndicate kth A classification pkCentral point vector, each central point vector corresponds to single order characteristic point, total k rank), will be every during quantization The corresponding characteristic point of a classification is quantified as the central point vector of the category, will such as cluster as classification pkCharacteristic point be quantified as vector zk。
In another embodiment, the quantization of K rank is carried out according to characteristic point of the preparatory preset K rank characteristic point to reservation, e.g., K rank characteristic point is set as q in advance1,q2,...,qk, and rule is set, the characteristic point of a certain specific region is quantified some The special website in one region is such as quantified as q by characteristic point1, the characteristic point in another region is quantified as q2Deng realizing all characteristic points K rank quantization.It is noted that we are not specifically limited the method for quantization here, theoretically for, as long as it can be according to Certain rule quantifies the characteristic point of reservation, that is, includes in the content of the present invention.
After quantization is completed, the characteristic point retained for every frame is counted, and obtains every rank characteristic point in K rank characteristic point Quantity, and then the feature vector of each frame is obtained according to the data of statistics.Specifically, if K rank characteristic point is respectively q1,q2,...,qk, In a frame, it counts on and is quantified as the first rank feature q1Characteristic point quantity be N1, it is quantified as second-order feature q2Feature points Amount is N2..., it is quantified as kth rank feature qkCharacteristic point quantity be Nk, then the feature vector of the frame is (N1,N2,...,Nk), with This obtains the feature vector of every frame.
After obtaining the feature vector of every frame, Kmeans cluster is carried out to the feature vector of each frame, obtains C classification, C= (A1,A2,...,Ac), wherein AcIndicate c-th of classification.To the sample size (each frame corresponding feature vector) in each classification It is counted, using the most one kind of sample size as the home court scape of video V, and obtains the feature vector of category central point (N1c,N2c,...,Nkc)。
Finally, calculating all frames and feature vector (N in video V1c,N2c,...,Nkc) Euclidean distance and be compared, Using the nearest frame of distance as the surface plot of video, the automatic extraction of video surface plot is completed.
In other embodiments, if the frame sequence quantity of video is more huge, the frame sequence of video is obtained in decompression Later, it is extracted according to the sequence frame that preset rules obtain decompression, obtains the set of frame, include according in the set later Frame carry out surface plot automatic extraction, to reduce calculation amount.For preset rules, can be set according to the actual situation, Such as, a frame, all frames that may range from video of extraction, or the partial frame in video are extracted every 1s (second) (representative one section of content in video).
The present invention also provides a kind of automatic draw-out devices of video cover, as shown in figure 3, the video cover extracts dress automatically It sets in 100 and includes:
Video decompression module 110 obtains sequence frame for unziping it to video；
Characteristic extracting module 120, for successively extracting the local feature region for each frame that video decompression module 110 obtains, and Calculate the intensity of each characteristic point；
Characteristic point reservation module 130 retains preset quantity for every frame according to the calculated result of characteristic extracting module 120 The stronger characteristic point of intensity；
Quantization modules 140, the characteristic point for retaining characteristic point reservation module 130 carry out the quantization of K rank；
Statistical module 150, for every frame, counts every in K rank characteristic point for the quantized result according to quantization modules 140 The quantity of rank characteristic point obtains the feature vector of each frame；
Cluster module 160, it is poly- that the feature vector for counting obtained all frames to statistical module 150 carries out Kmeans Class, and the most one kind of quantity will be assembled as the home court scape of video；
Surface plot extraction module 170 corresponds to the smallest frame of class center point apart from home court scape for extracting, and is made For the surface plot of video.
In the device 100, after the video V for obtaining needing to extract cover page, video decompression module 110 with i.e. by its into Row decompression obtains frame sequence, V=(F1,F2,F3,...,Fn), wherein n indicates the quantity of frame in video, FnIt indicates in video N-th frame.
After obtaining frame sequence, characteristic extracting module 120 successively extracts the local feature region of each frame, such as extracts SIFT feature Point or SURF characteristic point etc., and calculate the intensity of each characteristic point.The algorithm extracted for local feature region is not done specifically here It limits, arbitrary algorithm can be used and extract, for extracting SIFT feature, pass through SIFT algorithm meter in one frame of image Calculation obtains multiple SIFT features (vector description that each characteristic point is tieed up by one 128) according to the vector of 128 dimension The intensity of corresponding topical characteristic point is calculated.As shown in Figure 2, wherein Fig. 2 (a) is an original frame image, is calculated using SIFT Method extracts the SIFT feature of the frame image and calculates the intensity of each SIFT feature, as shown in Fig. 2 (b), specifically, in figure Great circle indicates the stronger SIFT feature of intensity, and ringlet indicates the weaker SIFT feature of intensity.
It is calculated after the intensity of each characteristic point (local feature region of extraction), characteristic point reservation module 130 retains The stronger characteristic point of intensity in every frame image, that is, be directed to every frame image, the intensity of all characteristic points be ranked up, according to pre- If the size of quantity retains local feature region.As in one example, preset quantity 100, then keeping characteristics point intensity Preceding 100 characteristic points (sequence is by by force to weak) in sequence, here without limitation to the occurrence of preset quantity, can be according to reality Situation setting, it is even more big to be such as set as 50,80,120,150,200.
After the characteristic point for remaining identical quantity for every frame image, quantization modules 140 carry out K to each characteristic point immediately Rank quantization.
For the method for quantization, in one embodiment, realized using the method for cluster, it is specific: cluster cell needle first Kmeans cluster is carried out to the characteristic point that all frames retain, after obtaining K cluster, the central point of each cluster is calculated, with This quantifying unit carries out the quantization of K rank to the characteristic point that all frames retain according to the central point of each cluster, and each classification is corresponding Characteristic point is quantified as the value of category central point.In one example, it is assumed that carry out Kmeans for the characteristic point that all frames retain Clustering K obtained cluster is respectively p1,p2,...,pk(pkIndicate k-th of classification), the central point vector of each classification is z1, z2,...,zk(zkIndicate k-th of classification pkCentral point vector, each central point vector corresponds to single order characteristic point, total k rank), During quantization, the corresponding characteristic point of each classification is quantified as to the central point vector of the category, will such as be clustered as classification pk Characteristic point be quantified as vector zk。
In another embodiment, the quantization of K rank is carried out according to characteristic point of the preparatory preset K rank characteristic point to reservation, e.g., K rank characteristic point is set as q in advance1,q2,...,qk, and rule is set, the characteristic point of a certain specific region is quantified some The special website in one region is such as quantified as q by characteristic point1, the characteristic point in another region is quantified as q2Deng realizing all characteristic points K rank quantization.It is noted that we are not specifically limited the method for quantization here, theoretically for, as long as it can be according to Certain rule quantifies the characteristic point of reservation, that is, includes in the content of the present invention.
After quantization is completed, the characteristic point that statistical module 150 retains for every frame is counted, and is obtained in K rank characteristic point The quantity of every rank characteristic point, and then the feature vector of each frame is obtained according to the data of statistics.Specifically, if K rank characteristic point is respectively q1,q2,...,qk, in a frame, count on and be quantified as the first rank feature q1Characteristic point quantity be N1, it is quantified as second-order feature q2Characteristic point quantity be N2..., it is quantified as kth rank feature qkCharacteristic point quantity be Nk, then the feature vector of the frame is (N1, N2,...,Nk), the feature vector of every frame is obtained with this.
After obtaining the feature vector of every frame, cluster module 160 carries out Kmeans cluster to the feature vector of each frame, obtains C classification, C=(A1,A2,...,Ac), wherein AcIndicate c-th of classification.To the sample size in each classification, (each frame is corresponding Feature vector) counted, using the most one kind of sample size as the home court scape of video V, and obtain category central point Feature vector (N1c,N2c,...,Nkc)。
Finally, surface plot extraction module 170 calculates all frames and feature vector (N in video V1c,N2c,...,Nkc) Europe Family name's distance is simultaneously compared, and using the nearest frame of distance as the surface plot of video, completes the automatic extraction of video surface plot.
In other embodiments, if the frame sequence quantity of video is more huge, in the automatic draw-out device 100 of video cover Frame abstraction module is also set, after decompression obtains the frame sequence of video, frame abstraction module is according to preset rules to decompressing To sequence frame extracted, obtain the set of frame, later according to include in the set frame carry out surface plot automatic extraction, To reduce calculation amount.It for preset rules, can be set according to the actual situation, e.g., extract a frame every 1s (second), extract All frames that may range from video, or the partial frame (representative one section of content in video) in video.

Claims (10)

1. a kind of automatic abstracting method of video cover characterized by comprising
S10 unzips it video to obtain sequence frame；
S20 successively extracts the local feature region of each frame, and calculates the intensity of each characteristic point；
S30 is directed to every frame, retains the stronger characteristic point of preset quantity intensity；
S40 carries out the quantization of K rank to the characteristic point of reservation；
S50 is directed to every frame, counts the quantity of every rank characteristic point in K rank characteristic point, obtains the feature vector of each frame；
S60 carries out Kmeans cluster to the feature vector of all frames, and will assemble the most one kind of quantity as the home court of video Scape；
S70, which is extracted, corresponds to the smallest frame of class center point apart from home court scape, and as the surface plot of video.
2. the automatic abstracting method of video cover as described in claim 1, which is characterized in that after step slo further include:
S11 is extracted according to the sequence frame that preset rules obtain decompression, obtains the set of frame；
In step S20, the local feature region of each frame in set is successively extracted.
3. the automatic abstracting method of video cover as described in claim 1, which is characterized in that in step S20, specifically: according to The secondary SIFT feature or SURF characteristic point for extracting each frame, and calculate the intensity of each characteristic point.
4. the automatic abstracting method of video cover as described in claims 1 or 2 or 4, which is characterized in that in step s 40, into one Step includes:
S41 carries out Kmeans cluster to the characteristic point of reservation；
S42 quantifies characteristic point according to the central point of all categories that cluster obtains.
5. the automatic abstracting method of video cover as described in claims 1 or 2 or 3, which is characterized in that in step s 40, specifically Are as follows: the quantization of K rank is carried out according to characteristic point of the preset K rank characteristic point to reservation.
6. a kind of automatic draw-out device of video cover characterized by comprising
Video decompression module obtains sequence frame for unziping it to video；
Characteristic extracting module for successively extracting the local feature region for each frame that video decompression module obtains, and calculates each spy Levy the intensity of point；
It is stronger to retain preset quantity intensity for every frame according to the calculated result of characteristic extracting module for characteristic point reservation module Characteristic point；
Quantization modules, the characteristic point for retaining characteristic point reservation module carry out the quantization of K rank；
Statistical module, for every frame, counts every rank characteristic point in K rank characteristic point for the quantized result according to quantization modules Quantity obtains the feature vector of each frame；
Cluster module, the feature vector of all frames for obtaining to statistical module counts carry out Kmeans cluster, and will aggregation Home court scape of the most one kind of quantity as video；
Surface plot extraction module corresponds to the smallest frame of class center point apart from home court scape for extracting, and as video Surface plot.
7. the automatic draw-out device of video cover as claimed in claim 6, which is characterized in that in the automatic draw-out device of video cover In further include:
Frame abstraction module, the sequence frame for being obtained according to preset rules to decompression extract, and obtain the set of frame；
In characteristic extracting module, the local feature region of each frame in the set that frame abstraction module extracts successively is extracted, and is calculated every The intensity of a characteristic point.
8. the automatic draw-out device of video cover as claimed in claim 6, which is characterized in that in characteristic extracting module, successively The SIFT feature or SURF characteristic point of each frame are extracted, and calculates the intensity of each characteristic point.
9. the automatic draw-out device of video cover as described in claim 6 or 7 or 8, which is characterized in that in quantization modules, packet It includes:
Cluster cell, for carrying out Kmeans cluster to the characteristic point retained characteristic point reservation module；
Quantifying unit, the central point of all categories for being clustered according to cluster cell quantify characteristic point.
10. the automatic draw-out device of video cover as described in claim 6 or 7 or 8, which is characterized in that in quantization modules, root The quantization of K rank is carried out according to characteristic point of the preset K rank characteristic point to reservation.
CN201811532062.1A 2018-12-14 2018-12-14 The automatic abstracting method of video cover and device Pending CN109635747A (en)

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CN107220585A (en) * 2017-03-31 2017-09-29 南京邮电大学 A kind of video key frame extracting method based on multiple features fusion clustering shots
CN107527010A (en) * 2017-07-13 2017-12-29 央视国际网络无锡有限公司 A kind of method that video gene is extracted according to local feature and motion vector

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CN107220585A (en) * 2017-03-31 2017-09-29 南京邮电大学 A kind of video key frame extracting method based on multiple features fusion clustering shots
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Publication number Priority date Publication date Assignee Title
WO2021004247A1 (en) * 2019-07-11 2021-01-14 北京字节跳动网络技术有限公司 Method and apparatus for generating video cover and electronic device
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