CN111225274B - Photo music video arrangement system based on deep learning - Google Patents

Photo music video arrangement system based on deep learning Download PDF

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CN111225274B
CN111225274B CN201911204406.0A CN201911204406A CN111225274B CN 111225274 B CN111225274 B CN 111225274B CN 201911204406 A CN201911204406 A CN 201911204406A CN 111225274 B CN111225274 B CN 111225274B
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CN111225274A (en
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龚俊衡
徐莹
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Chengdu Pinguo Technology Co Ltd
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Abstract

The invention discloses a photo music video arrangement system based on deep learning, which comprises the following steps: s1, inputting a photo group and a video group, wherein the number of the photo group and the video group is arbitrary, and music is needed; s2, based on the information such as music rhythm, the music is segmented into paragraphs with different lengths based on the information such as rhythm; s3, extracting key frames of each video in the video group in a manual or automatic mode; s4, extracting the depth feature of the picture group P by using a convolutional neural network or other depth/non-depth machine learning algorithms, and simultaneously calculating the key frame of the video in the video group; and S5, selecting any photo as a starting photo, and calculating the arrangement of the photo and the video in the music paragraph by using a recurrent neural network or other depth/non-depth machine learning algorithms. The invention automatically performs the cut-off of music, analyzes the key contents of photos and videos, and fuses the photo videos based on the music cut-off to achieve the purpose of rapidly and intelligently making the music photo videos.

Description

Photo music video arrangement system based on deep learning
Technical Field
The invention relates to the technical field of information processing, in particular to a photo and music video arrangement system based on deep learning.
Background
In the age of machine learning application becoming more mature, video production is still a relatively complex process, and requires certain arranging and related resource collecting and arranging capabilities for related personnel, especially trying to merge photos into video, and recomposing photos, animation, special effects and the like will greatly increase the production complexity.
Disclosure of Invention
The invention aims to provide a photo music video arrangement system based on deep learning, which can rapidly and intelligently make music photo videos.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
the invention discloses a photo music video arrangement system based on deep learning, S1, data preparation, comprising:
picture group P ═ P0，p1…pnVideo set S ═ S0，s1…smMusic;
s2, segmenting the music into music paragraphs Q ═ Q0，q1…qk}；
S3, extracting P' ═ cnn _ deep _ feature (P) from the group of pictures using the deep neural network;
s4, extracting S' ═ cnn _ deep _ feature (S) from the video group using the deep neural network;
s5, randomly selecting a photo to be placed in any music paragraph as a starting photo
Figure GDA0003314966870000021
S6, setting the material distribution set of the music paragraph as
Figure GDA0003314966870000022
Is provided with
Figure GDA0003314966870000023
In
Figure GDA0003314966870000024
Is located at a paragraph position of
Figure GDA0003314966870000025
S7, position from paragraph
Figure GDA0003314966870000026
Start of calculation
Figure GDA0003314966870000027
All the remaining positions in (1), wherein the remaining positions are denoted as q, q ∈ {0 … k } and
Figure GDA0003314966870000028
calculating the best photo or the best video which should be put into the rest position;
s8, when
Figure GDA0003314966870000029
After all the positions in the map are determined to be the distributed material,
Figure GDA00033149668700000210
the final result is obtained.
Preferably, in step S2, the segmentation of music is based on music tempo, using manual or automated tools.
Preferably, the best picture is confirmed using the following function,
Figure GDA00033149668700000211
wherein q is a specific paragraph and p is any one of the photo collections not present in the photo collection
Figure GDA00033149668700000212
The photograph appearing in (1).
Preferably, the best video is identified using the following function,
Figure GDA00033149668700000213
wherein q is a specific paragraph and p is any one of the photo collections not present in the photo collection
Figure GDA00033149668700000214
Picture in, s is videoAny in the set is not
Figure GDA00033149668700000215
The video that appears in (1).
Preferably, the best picture or the best video is determined as the best material, and f (p, s) is max (p, s).
The invention has the beneficial effects that:
and performing point cutting on the music by adopting a manual or automatic tool, analyzing key contents of the photos and the videos by using a deep neural network, and fusing the photos and the videos based on the music point cutting by using a circulating neural network so as to achieve the aim of rapidly and intelligently manufacturing the music photo videos.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the present invention comprises the steps of:
s1, preparing data, including:
picture group P ═ P0，p1…pnVideo set S ═ S0，s1…smMusic;
s2, segmenting the music into music paragraphs Q ═ Q0，q1…qkDividing music based on music rhythm by using manual or automatic tool;
s3, extracting P' ═ cnn _ deep _ feature (P) from the group of pictures using the deep neural network;
wherein P' refers to the depth feature of the photo after cnn _ deep _ feature () extraction, and the specific depth feature content depends on the network structure used in cnn _ deep _ feature;
the cnn _ deep _ feature refers to a photo depth feature function calculated by a deep convolutional neural network (cnn algorithm) using a back propagation principle, such as full connectivity, and the like, which are simultaneously or independently used;
s4, extracting S' ═ cnn _ deep _ feature (S) from the video group using the deep neural network;
wherein S' refers to a picture depth feature containing frame information after extraction by cnn _ deep _ feature (); s ' is similar to P ' and is different from P ' in that a single picture of P is input, multi-frame video information which is changed into S is input, the extracted frame information is calculated frame by frame in a processing flow similar to P in a mode of frame extraction, frame by frame and the like, and S ' similar to P ' is formed, and the difference is that S ' data has one more dimension than P ', namely all extracted frames;
s5, randomly selecting a photo to be placed in any music paragraph as a starting photo
Figure GDA0003314966870000031
S6, setting the material distribution set of the music paragraph as
Figure GDA0003314966870000032
Is provided with
Figure GDA0003314966870000033
In
Figure GDA0003314966870000034
Is located at a paragraph position of
Figure GDA0003314966870000035
S7, position from paragraph
Figure GDA0003314966870000036
Start of calculation
Figure GDA0003314966870000037
All the remaining positions in (1), wherein the remaining positions are denoted as q, q ∈ {0 … k } and
Figure GDA0003314966870000041
calculate the best picture or best video that should be put in the remaining position,
s7.1, confirm the best picture using the following function,
Figure GDA0003314966870000042
wherein q is a specific paragraph and p is any one of the photo collections not present in the photo collection
Figure GDA0003314966870000043
The picture that appears in (a) is,
s7.2, identify the best video using the following function,
Figure GDA0003314966870000044
wherein q is a specific paragraph and p is any one of the photo collections not present in the photo collection
Figure GDA0003314966870000045
S is any picture in the video set that is not present in
Figure GDA0003314966870000046
The video that is present in (1) is,
s7.3 identifies the best picture or video as the best material, where f (p, S) is max (p, S);
s8, when
Figure GDA0003314966870000047
After all the positions in the map are determined to be the distributed material,
Figure GDA0003314966870000048
the final result is obtained.
In the process of the actual use of the rubber,
suppose we have 3 photographs, P1, P2, P3,
suppose we have 7 videos, S1, S2, S3, S4, S5, S6, S7,
assuming that we have music with segmented paragraphs Q1, Q2, Q3, Q4, Q5, we need to fill in 5 paragraphs with photos or videos respectively,
through steps S1-S4, the data set by us are all features, and can be directly input by a machine learning model, wherein cnn _ deep _ feature can be any existing convolution/non-convolution image neural network including but not limited to various existing models of open source and closed source, and the features can be any layer of output after the convolution layer (specific layer needs to be manually selected)
RNN the model is a sequence model that can be any existing sequence model including but not limited to RNN, LSTM, GRU, etc. by comparing the context in the sequence, the best match unknown can be calculated,
after randomly selecting a picture, the paragraph status may be as follows, via step S5:
Q1 Q2 Q3 Q4 Q5
is not distributed Is not distributed Q2 Is not distributed Is not distributed
Wherein Q2 is a randomly assigned initial photograph
In the calculation flow of S7, we need to calculate the best paragraph materials one by one for Q1, Q2, Q4, and Q5 until all Q' S are filled.
The present invention is capable of other embodiments, and various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention.

Claims (5)

1. A photo music video arrangement system based on deep learning is characterized by comprising the following steps:
s1, preparing data, including:
picture group P ═ P0，p1…pnVideo set S ═ S0，s1…smMusic;
s2, segmenting the music into music paragraphs Q ═ Q0，q1…qk}；
S3, extracting P' ═ cnn _ deep _ feature (P) from the group of pictures using the deep neural network;
wherein P' refers to the depth feature of the photo after cnn _ deep _ feature () extraction, and the specific depth feature content depends on the network structure used in cnn _ deep _ feature;
the cnn _ deep _ feature refers to a photo depth feature function calculated by a deep convolutional neural network (cnn algorithm) using a back propagation principle, such as full connectivity, and the like, which are simultaneously or independently used;
s4, extracting S' ═ cnn _ deep _ feature (S) from the video group using the deep neural network;
wherein S' refers to a picture depth feature containing frame information after extraction by cnn _ deep _ feature ();
s5, randomly selecting a photo to be placed in any music paragraph as a starting photo
Figure FDA0003314966860000011
S6, material of music paragraphAn allocation set is set as
Figure FDA0003314966860000012
Is provided with
Figure FDA0003314966860000013
In
Figure FDA0003314966860000014
Is located at a paragraph position of
Figure FDA0003314966860000015
S7, position from paragraph
Figure FDA0003314966860000016
Start of calculation
Figure FDA0003314966860000017
All the remaining positions in (1), where the remaining positions are denoted as q, q ∈ {0.. k } and
Figure FDA0003314966860000018
calculating the best photo or the best video which should be put into the rest position;
s8, when
Figure FDA0003314966860000019
After all the positions in the map are determined to be the distributed material,
Figure FDA00033149668600000110
the final result is obtained.
2. The deep learning based photo music video layout system of claim 1, wherein: in step S2, the segmentation of music is based on music tempo, using manual or automated tools.
3. The deep learning based photo music video layout system of claim 1, wherein: in step S7, the best photograph is confirmed using the following function,
Figure FDA0003314966860000021
wherein q is a specific paragraph and p is any one of the photo collections not present in the photo collection
Figure FDA0003314966860000022
The photograph appearing in (1).
4. The deep learning based photo music video layout system of claim 3, wherein: in step S7, the best video is identified using the following function,
Figure FDA0003314966860000023
wherein q is a specific paragraph and p is any one of the photo collections not present in the photo collection
Figure FDA0003314966860000024
S is any picture in the video set that is not present in
Figure FDA0003314966860000025
The video that appears in (1).
5. The deep learning based photo music video layout system of claim 4, wherein: in step S7, the best photo or the best video is determined as the best material,
f(p，s)＝max(p，s)。
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