CN110688526A - Short video recommendation method and system based on key frame identification and audio textualization - Google Patents
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Abstract
The disclosure discloses a short video recommendation method and system based on key frame identification and audio textualization, comprising the following steps: acquiring short videos to be recommended; uniformly dividing the short video into a plurality of video segments according to a time sequence; extracting key frames of each video clip, identifying images of each key frame, and extracting image classification labels and subtitle text information; extracting corresponding audio information from each video clip, and extracting corresponding audio text information from the audio information; integrating the obtained image classification labels, the subtitle text information and the audio text information to obtain text information of the short video to be recommended; analyzing the word frequency of each word from the short video text information to be recommended, sequencing each word according to the word frequency from large to small, and outputting M words in the front of the sequence as the keywords of the short video to be recommended; and providing a recommendation result for the user by utilizing a collaborative filtering algorithm according to the keywords of the short video to be recommended and keywords prestored by the user.
Description
Technical Field
The disclosure relates to the technical field of short video recommendation, and in particular to a short video recommendation method and system based on key frame identification and audio textualization.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Compared with the traditional video, the short video has the defects that the video time is short (mostly within 15 seconds), valuable text descriptions are lacked, and due to the problem of the short video time, in the process of realizing the disclosure, the inventor finds that the text descriptions aiming at the video cannot be effectively extracted by the traditional video analysis mode in the prior art. Therefore, for the intelligent recommendation of the short video news contents, algorithm calculation cannot be performed according to the information provided by the short video to generate the recommended contents.
Disclosure of Invention
In order to solve the deficiencies of the prior art, the present disclosure provides a short video recommendation method and system based on key frame identification and audio textualization;
in a first aspect, the present disclosure provides a short video recommendation method based on keyframe identification and audio textualization;
a short video recommendation method based on key frame identification and audio textualization comprises the following steps:
acquiring short videos to be recommended; uniformly dividing the short video into a plurality of video segments according to a time sequence;
extracting key frames of each video clip, identifying images of each key frame, and extracting image classification labels and subtitle text information;
extracting corresponding audio information from each video clip, and extracting corresponding audio text information from the audio information;
integrating the obtained image classification labels, the subtitle text information and the audio text information to obtain text information of the short video to be recommended;
analyzing the word frequency of each word from the short video text information to be recommended, sequencing each word according to the word frequency from large to small, and outputting M words in the front of the sequence as the keywords of the short video to be recommended; m is a positive integer;
and providing a recommendation result for the user by utilizing a collaborative filtering algorithm according to the keywords of the short video to be recommended and keywords prestored by the user.
In a second aspect, the present disclosure also provides a short video recommendation system based on keyframe identification and audio textualization;
a short video recommendation system based on keyframe identification and audio textualization comprising:
a segmentation module configured to: acquiring short videos to be recommended; uniformly dividing the short video into a plurality of video segments according to a time sequence;
a key frame extraction module configured to: extracting key frames of each video clip, identifying images of each key frame, and extracting image classification labels and subtitle text information;
an audio extraction module configured to: extracting corresponding audio information from each video clip, and extracting corresponding audio text information from the audio information;
a text integration module configured to: integrating the obtained image classification labels, the subtitle text information and the audio text information to obtain text information of the short video to be recommended;
a keyword output module configured to: analyzing the word frequency of each word from the short video text information to be recommended, sequencing each word according to the word frequency from large to small, and outputting M words in the front of the sequence as the keywords of the short video to be recommended; m is a positive integer;
a recommendation output module configured to: and providing a recommendation result for the user by utilizing a collaborative filtering algorithm according to the keywords of the short video to be recommended and keywords prestored by the user.
In a third aspect, the present disclosure also provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method of the first aspect.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method of the first aspect.
Compared with the prior art, the beneficial effect of this disclosure is:
the video description information is efficiently extracted from the short video with the length not more than 30 seconds, and an effective recommendation basis is provided for a collaborative filtering recommendation algorithm.
The scheme can solve the problem of intelligent recommendation of multimedia contents such as short videos and the like, and provides personalized short video contents for the user according to the user's preference.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of the method of the first embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiment I provides a short video recommendation method based on key frame identification and audio textualization;
as shown in fig. 1, the short video recommendation method based on key frame identification and audio texting includes:
s1: acquiring short videos to be recommended; uniformly dividing the short video into a plurality of video segments according to a time sequence;
s2: extracting key frames of each video clip, identifying images of each key frame, and extracting image classification labels and subtitle text information;
s3: extracting corresponding audio information from each video clip, and extracting corresponding audio text information from the audio information;
s4: integrating the obtained image classification labels, the subtitle text information and the audio text information to obtain text information of the short video to be recommended;
s5: analyzing the word frequency of each word from the short video text information to be recommended, sequencing each word according to the word frequency from large to small, and outputting M words in the front of the sequence as the keywords of the short video to be recommended; m is a positive integer;
s6: and providing a recommendation result for the user by utilizing a collaborative filtering algorithm according to the keywords of the short video to be recommended and keywords prestored by the user.
As one or more embodiments, in S1, the short video is divided into several video segments, for example, 100 video segments, according to the time sequence.
In one or more embodiments, in S2, the key frame extraction is performed on each video segment, and a pixel frame averaging method or a histogram frame averaging method is used.
As one or more embodiments, in S2, performing image recognition on each key frame, and extracting an image classification label; the method comprises the following specific steps: and inputting each key frame into a pre-trained first convolutional neural network, and outputting an image classification label.
It should be understood that the training process of the pre-trained first convolutional neural network is:
constructing a first convolution neural network and a training set; the training set is an image of a known image label;
and inputting the training set into the first convolution neural network for training, and stopping training when the loss function reaches the minimum value to obtain the trained first convolution neural network.
As one or more embodiments, in S2, performing image recognition on each key frame to extract subtitle text information; the method comprises the following specific steps: and inputting each key frame into a pre-trained second convolutional neural network, and outputting subtitle text information.
It should be understood that the training process of the pre-trained second convolutional neural network is:
constructing a second convolutional neural network and a training set; the training set is an image with subtitles of known subtitle text information;
and inputting the training set into a second convolutional neural network for training, and stopping training when the loss function reaches the minimum value to obtain the trained second convolutional neural network.
As one or more embodiments, in S3, the extracting the corresponding audio information for each video segment is implemented by using FFMPEG software.
As one or more embodiments, in S3, after the step of extracting corresponding audio information for each video segment, and before the step of extracting corresponding audio text information from the audio information, the method further includes: and denoising the audio information.
As one or more embodiments, after S4 and before S5, the method further includes: and denoising the text information of the short video to be recommended.
Further, the denoising processing is performed on the text information of the short video to be recommended, and the specific steps include:
s401: performing part-of-speech preliminary screening on text information of a short video to be recommended based on a part-of-speech analysis algorithm of TF-TDF, removing first invalid words and reserving the first valid words;
s402: performing part-of-speech statistics on the text information of the short video to be recommended based on a statistical algorithm, removing second invalid words and keeping the second valid words;
s403: and performing intersection processing on the first effective word and the second effective word to obtain the text information of the short video to be recommended after denoising processing.
And the optimization of the noise reduction effect is realized by filtering the intersection of the two result sets.
It should be understood that the first invalid word includes at least: one or more of adverb, preposition, conjunctions, helpwords, or sigh; the first valid word at least comprises: nouns or verbs. A second invalid word comprising: a particle; a second valid word comprising: a noun.
As one or more embodiments, in S6, providing a recommendation result to a user by using a collaborative filtering algorithm according to the keywords of the short video to be recommended and the keywords pre-stored by the user; the method comprises the following specific steps:
calculating the similarity between the keywords of the short video to be recommended and keywords prestored by the user according to the keywords of the short video to be recommended and the keywords prestored by the user; if the similarity exceeds a set threshold, judging that the current short video to be recommended is the short video which is interested by the user; otherwise, judging that the current short video to be recommended is not the short video which is interested by the user and not recommending.
As one or more embodiments, after S6, the method further includes:
and comparing the recommended short video with the recommended video list, deleting the short video to be recommended if the short video to be recommended already exists in the recommended video list, and returning to the step S1 to perform recommendation processing of the next short video to be recommended.
It should be understood that the recommended video list includes: video title, video distribution time, video source, video address.
The second embodiment also provides a short video recommendation system based on key frame identification and audio textualization;
a short video recommendation system based on keyframe identification and audio textualization comprising:
a segmentation module configured to: acquiring short videos to be recommended; uniformly dividing the short video into a plurality of video segments according to a time sequence;
a key frame extraction module configured to: extracting key frames of each video clip, identifying images of each key frame, and extracting image classification labels and subtitle text information;
an audio extraction module configured to: extracting corresponding audio information from each video clip, and extracting corresponding audio text information from the audio information;
a text integration module configured to: integrating the obtained image classification labels, the subtitle text information and the audio text information to obtain text information of the short video to be recommended;
a keyword output module configured to: analyzing the word frequency of each word from the short video text information to be recommended, sequencing each word according to the word frequency from large to small, and outputting M words in the front of the sequence as the keywords of the short video to be recommended; m is a positive integer;
a recommendation output module configured to: and providing a recommendation result for the user by utilizing a collaborative filtering algorithm according to the keywords of the short video to be recommended and keywords prestored by the user.
In a third embodiment, the present embodiment further provides an electronic device, which includes a memory, a processor, and computer instructions stored in the memory and executed on the processor, where the computer instructions, when executed by the processor, implement the steps of the method in the first embodiment.
In a fourth embodiment, the present embodiment further provides a computer-readable storage medium for storing computer instructions, and the computer instructions, when executed by a processor, perform the steps of the method in the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. The short video recommendation method based on key frame identification and audio textualization is characterized by comprising the following steps of:
acquiring short videos to be recommended; uniformly dividing the short video into a plurality of video segments according to a time sequence;
extracting key frames of each video clip, identifying images of each key frame, and extracting image classification labels and subtitle text information;
extracting corresponding audio information from each video clip, and extracting corresponding audio text information from the audio information;
integrating the obtained image classification labels, the subtitle text information and the audio text information to obtain text information of the short video to be recommended;
analyzing the word frequency of each word from the short video text information to be recommended, sequencing each word according to the word frequency from large to small, and outputting M words in the front of the sequence as the keywords of the short video to be recommended; m is a positive integer;
and providing a recommendation result for the user by utilizing a collaborative filtering algorithm according to the keywords of the short video to be recommended and keywords prestored by the user.
2. The method of claim 1, wherein the key frame extraction is performed for each video segment using pixel frame averaging or histogram frame averaging.
3. The method of claim 1, wherein image recognition is performed on each key frame to extract an image classification tag; the method comprises the following specific steps: inputting each key frame into a pre-trained first convolutional neural network, and outputting an image classification label;
performing image recognition on each key frame, and extracting subtitle text information; the method comprises the following specific steps: and inputting each key frame into a pre-trained second convolutional neural network, and outputting subtitle text information.
4. The method of claim 1, wherein after the step of extracting corresponding audio information for each video segment, and before the step of extracting corresponding audio text information from the audio information, further comprising: and denoising the audio information.
5. The method as claimed in claim 1, wherein after the obtained image classification label, subtitle text information and audio text information are integrated to obtain the text information of the short video to be recommended, the word frequency of each word is analyzed from the text information of the short video to be recommended, each word is sorted according to the word frequency from large to small, and before M words in the top of the order are output as the keywords of the short video to be recommended, the method further comprises: denoising text information of the short video to be recommended;
the method comprises the following steps of denoising text information of a short video to be recommended, wherein the denoising method comprises the following specific steps:
s401: performing part-of-speech preliminary screening on text information of a short video to be recommended based on a part-of-speech analysis algorithm of TF-TDF, removing first invalid words and reserving the first valid words;
s402: performing part-of-speech statistics on the text information of the short video to be recommended based on a statistical algorithm, removing second invalid words and keeping the second valid words;
s403: and performing intersection processing on the first effective word and the second effective word to obtain the text information of the short video to be recommended after denoising processing.
6. The method as claimed in claim 1, wherein the providing of the recommendation result for the user by using the collaborative filtering algorithm is based on the keyword of the short video to be recommended and the keyword pre-stored by the user, and the providing of the recommendation result for the user by using the collaborative filtering algorithm is based on the keyword of the short video to be recommended and the keyword pre-stored by the user; the method comprises the following specific steps:
calculating the similarity between the keywords of the short video to be recommended and keywords prestored by the user according to the keywords of the short video to be recommended and the keywords prestored by the user; if the similarity exceeds a set threshold, judging that the current short video to be recommended is the short video which is interested by the user; otherwise, judging that the current short video to be recommended is not the short video which is interested by the user and not recommending.
7. The method as claimed in claim 1, wherein after providing the recommendation result for the user by using the collaborative filtering algorithm according to the keywords of the short video to be recommended and the keywords pre-stored by the user, the method further comprises:
comparing the recommended short video with the recommended video list, if the short video to be recommended exists in the recommended video list, deleting the short video to be recommended, and returning to the step of obtaining the short video to be recommended; and performing recommendation processing of the next short video to be recommended.
8. A short video recommendation system based on key frame identification and audio textualization is characterized by comprising the following steps:
a segmentation module configured to: acquiring short videos to be recommended; uniformly dividing the short video into a plurality of video segments according to a time sequence;
a key frame extraction module configured to: extracting key frames of each video clip, identifying images of each key frame, and extracting image classification labels and subtitle text information;
an audio extraction module configured to: extracting corresponding audio information from each video clip, and extracting corresponding audio text information from the audio information;
a text integration module configured to: integrating the obtained image classification labels, the subtitle text information and the audio text information to obtain text information of the short video to be recommended;
a keyword output module configured to: analyzing the word frequency of each word from the short video text information to be recommended, sequencing each word according to the word frequency from large to small, and outputting M words in the front of the sequence as the keywords of the short video to be recommended; m is a positive integer;
a recommendation output module configured to: and providing a recommendation result for the user by utilizing a collaborative filtering algorithm according to the keywords of the short video to be recommended and keywords prestored by the user.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of any one of claims 1 to 7.
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