阅读说明：本技术 一种动态更新视频标签的方法、装置及电子设备 (Method and device for dynamically updating video label and electronic equipment ) 是由 张志伟 王希爱 李焱 郑仲奇 于 2020-04-23 设计创作，主要内容包括：本发明提供一种动态更新视频标签的方法、装置及电子设备,该方法包括：接收平台账户上传的待标注视频数据,从平台账户之前上传的视频数据中,获取标签视频数据集合外的缓冲视频数据,标签视频数据集合包括至少一个标注视频标签的标签视频数据；采用分类算法对待标注视频数据及缓冲视频数据进行分类,根据分类结果确定触发视频标签更新时,确定各分类相对标签视频数据集合的变化率；确定变化率大于设定阈值的分类对应的新视频标签,利用新视频标签更新标注的视频标签,利用该分类中的视频数据更新标签视频数据。本发明提供的动态更新视频标签的方法、装置及电子设备,解决了现有确定视频标签的方法对平台账户的代表性数据感知和更新不及时的问题。(The invention provides a method, a device and electronic equipment for dynamically updating video tags, wherein the method comprises the following steps: receiving video data to be labeled uploaded by a platform account, and acquiring buffered video data outside a label video data set from the video data uploaded by the platform account before, wherein the label video data set comprises at least one label video data of a label of a labeled video; classifying the video data to be marked and the buffered video data by adopting a classification algorithm, and determining the change rate of each classification relative to a label video data set when determining to trigger the update of a video label according to a classification result; and determining a new video label corresponding to the classification with the change rate larger than the set threshold value, updating the labeled video label by using the new video label, and updating the labeled video data by using the video data in the classification. The method, the device and the electronic equipment for dynamically updating the video tag solve the problem that the representative data of a platform account is not sensed and updated timely by the conventional method for determining the video tag.)

1. A method for dynamically updating video tags, comprising:

receiving video data to be labeled uploaded by a platform account, and acquiring buffered video data outside a label video data set from the video data uploaded by the platform account before, wherein the label video data set comprises at least one label video data of a label of a labeled video;

classifying the video data to be marked and the buffered video data by adopting a classification algorithm, and determining the change rate of each classification relative to a label video data set when determining to trigger the update of a video label according to a classification result; and determining a new video label corresponding to the classification with the change rate larger than the set threshold value, updating the labeled video label by using the new video label, and updating the labeled video data by using the video data in the classification.

2. The method according to claim 1, wherein the classifying the video data to be labeled and the buffered video data by using a classification algorithm comprises:

inputting the video data to be marked and the buffer video data into a characteristic data extraction model, and respectively extracting characteristic data of the video data to be marked and the buffer video data;

and taking the extracted characteristic data as a sample set, and clustering the extracted characteristic data by adopting a clustering method.

3. The method of claim 1, wherein determining to trigger a video tag update according to the classification result comprises:

and determining whether to trigger video tag updating according to the proportion of the number of the video data in each category to the total number of the video data, wherein the video data in each category is at least one of the video data to be marked and the buffered video data, and the total number of the video data is the total number of the video data to be marked and the buffered video data.

4. The method of claim 3, wherein determining whether to trigger the video tag update according to the ratio of the number of video data to the total number of video data in each category comprises:

taking the ratio of the number of the video data in the category of the video data to be labeled to the total number of the video data as the probability of triggering the update of the video label, and triggering the update of the video label according to the probability; or

And if the ratio of the number of the video data in the category of the video data to be labeled to the total number of the video data exceeds a set threshold value, triggering the video label to be updated.

5. The method of claim 1, wherein obtaining buffered video data outside the tagged video data set from video data previously uploaded by the platform account comprises:

and according to the interval between the time of uploading the video data before the platform account and the current time, obtaining the buffered video data outside the tag video data set according to the sequence from small to large of the interval.

6. The method of claim 1, wherein determining that a video tag update is triggered further comprises:

marking the video data to be marked as buffer video data;

and deleting the buffered video data with the largest interval from the current time in the buffered video data.

7. The method of claim 1, wherein determining a rate of change of each classification relative to a set of tagged video data when determining to trigger a video tag update comprises:

selecting a classification with the largest number of video data in the classifications, and determining the change rate of the classification relative to the label video data set; or

And selecting N classes according to the sequence of the number of the video data in the classes from most to less, and determining the change rate of each class relative to the label video data set, wherein N is a positive integer greater than 1.

8. An apparatus for dynamically updating video tags, comprising:

the system comprises a data acquisition module, a storage module and a display module, wherein the data acquisition module is used for receiving video data to be labeled uploaded by a platform account and acquiring buffered video data outside a label video data set from the video data uploaded by the platform account before, and the label video data set comprises at least one label video data of a label of a labeled video;

the classification module is used for classifying the video data to be labeled and the buffered video data by adopting a classification algorithm, and determining the change rate of each classification relative to a label video data set when determining to trigger video label updating according to a classification result;

and the updating module is used for determining a new video label corresponding to the classification with the change rate larger than the set threshold value, updating the labeled video label by using the new video label and updating the labeled video data by using the video data in the classification.

9. An electronic device for dynamically updating video tags, comprising: a memory and a processor;

wherein the memory is for storing a computer program;

the processor is used for executing the program in the memory and realizing the steps of the method according to any one of claims 1 to 7.

10. A computer program medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

Technical Field

The present invention relates to the field of short video data application, and in particular, to a method and an apparatus for dynamically updating a video tag, and an electronic device.

Background

With the development of a UGC (User Generated Content) platform, the application of short video APPs is rapidly increasing, so that a server side is exposed to a large amount of data uploading. Under the background of a big data era, a large amount of short video information is filled in a video website or short video software, and how to accurately portray platform accounts uploading short video data becomes an important requirement for short video service recommendation. The short video label is a user portrait, the representative short video of the platform account is abstracted into the label, and the label is utilized to embody the image of the platform account, thereby providing targeted service for the platform account.

The short video tag is generally determined according to the content of the short video data uploaded by the platform account, and after the short video tag is determined, a tag can be further added to the short video data uploaded by the platform account. The method for determining the short video tag is mainly used for carrying out annotation updating on the short video tag on a platform account in a staged manner, the method has the problems of high annotation cost and low efficiency when the number of the platform accounts is too large, and in the process of determining the short video tag, the content of video data uploaded by the platform account may change along with the change of time. For example, a platform account that frequently uploads video related to games may be changed to upload video related to food after a certain period of time, and therefore, when the representative video data of the platform account changes, the problem of untimely perception and update may occur. In addition, when the video data uploaded by a certain platform account changes, the short video tags which cannot represent the platform account need to be changed, for example, the platform account which frequently uploads game videos is most relevant to games, but the platform account is not prevented from uploading other types of video data at a certain time. Therefore, there is a need to propose a more timely and more efficient and accurate method of updating representative data of a platform account.

In summary, the existing method for updating the video tag has the problem that the representative data of the platform account is not sensed and updated timely.

Disclosure of Invention

The invention provides a method and a device for dynamically updating a video tag and electronic equipment, which are used for solving the problems of untimely perception and update of representative data of a platform account in the conventional method for determining the video tag.

According to a first aspect of the embodiments of the present invention, there is provided a method for dynamically updating a video tag, the method including:

receiving video data to be labeled uploaded by a platform account, and acquiring buffered video data outside a label video data set from the video data uploaded by the platform account before, wherein the label video data set comprises at least one label video data of a label of a labeled video;

classifying the video data to be marked and the buffered video data by adopting a classification algorithm, and determining the change rate of each classification relative to a label video data set when determining to trigger the update of a video label according to a classification result;

and determining a new video label corresponding to the classification with the change rate larger than the set threshold value, updating the labeled video label by using the new video label, and updating the labeled video data by using the video data in the classification.

According to a second aspect of the embodiments of the present invention, there is provided an apparatus for dynamically updating a video tag, including:

the system comprises a data acquisition module, a storage module and a display module, wherein the data acquisition module is used for receiving video data to be labeled uploaded by a platform account and acquiring buffered video data outside a label video data set from the video data uploaded by the platform account before, and the label video data set comprises at least one label video data of a label of a labeled video;

the classification module is used for classifying the video data to be labeled and the buffered video data by adopting a classification algorithm, and determining the change rate of each classification relative to a label video data set when determining to trigger video label updating according to a classification result;

and the updating module is used for determining a new video label corresponding to the classification with the change rate larger than the set threshold value, updating the labeled video label by using the new video label and updating the labeled video data by using the video data in the classification.

Optionally, the classifying module classifies the video data to be labeled and the buffered video data by using a classification algorithm, including:

inputting the video data to be marked and the buffer video data into a characteristic data extraction model, and respectively extracting characteristic data of the video data to be marked and the buffer video data;

and taking the extracted characteristic data as a sample set, and clustering the extracted characteristic data by adopting a clustering method.

Optionally, the determining, by the classification module, to trigger the video tag update according to the classification result includes:

and determining whether to trigger video tag updating according to the proportion of the number of the video data in each category to the total number of the video data, wherein the video data in each category is at least one of the video data to be marked and the buffered video data, and the total number of the video data is the total number of the video data to be marked and the buffered video data.

Optionally, the classifying module determines whether to trigger the video tag update according to a ratio of the number of the video data in each classification to the total number of the video data, and includes:

taking the ratio of the number of the video data in the category of the video data to be labeled to the total number of the video data as the probability of triggering the update of the video label, and triggering the update of the video label according to the probability; or

And if the ratio of the number of the video data in the category of the video data to be labeled to the total number of the video data exceeds a set threshold value, triggering the video label to be updated.

Optionally, the data obtaining module is specifically configured to:

if the video data uploaded before the platform account does not have the label video data set, sending annotation prompt information, and receiving the fed back video label and the corresponding label video data set, or

If the label video data set does not exist in the video data uploaded before the platform account and the number of the video data exceeds the set number, classifying the uploaded video data, labeling the video labels according to the classification result and determining the corresponding label video data.

Optionally, the obtaining, by the data obtaining module, buffered video data outside the tagged video data set from video data uploaded by the platform account before, includes:

and according to the interval between the time of uploading the video data before the platform account and the current time, obtaining the buffered video data outside the tag video data set according to the sequence from small to large of the interval.

Optionally, when determining that the video tag is triggered to be updated, the classification module is specifically configured to:

marking the video data to be marked as buffer video data;

and deleting the buffered video data with the largest interval from the current time in the buffered video data.

Optionally, when determining that the video tag update is triggered, the classification module determines a change rate of each classification with respect to the tag video data set, including:

selecting a classification with the largest number of video data in the classifications, and determining the change rate of the classification relative to the label video data set; or

And selecting N classes according to the sequence of the number of the video data in the classes from most to less, and determining the change rate of each class relative to the label video data set, wherein N is a positive integer greater than 1.

Optionally, the classifying module determines a rate of change of each classification with respect to the set of tagged video data, including:

selecting M video data according to the sequence of the distances from each video data in the classification to the corresponding classification center from small to large, wherein M is a preset positive integer larger than 1;

removing video data belonging to the same video label as the label video data set from the M video data to obtain a new set;

and dividing the number of the video data in the new set by the number of the label video data in the label video data set to obtain the change rate of the classification relative to the label video data set.

Optionally, the classifying module selects M pieces of video data according to a sequence from small to large of a distance from each piece of video data to a corresponding classification center in the classification, including:

averaging the characteristic data corresponding to the video data in the classification to obtain a classification center;

determining the cosine distance from the feature data corresponding to each video data in the classification to the classification center, and selecting M video data from the classification according to the sequence of the cosine distances from small to large.

Optionally, if the classification module selects a classification with the largest number of video data in the classifications, determining a corresponding new video tag according to the video data in the classification with the change rate greater than the set threshold, including:

if the change rate of the classification relative to the label video data set is larger than a set threshold value, determining a corresponding new video label according to the video data in the classification;

and updating the labeled video tags by using the new video tags, and selecting M video data from the classification to update the tagged video data.

Optionally, if the classification module selects N classifications in the order of the number of video data in the classifications, determining a corresponding new video tag according to the video data in the classification with the change rate greater than the set threshold, including:

screening out the classification of which the change rate is greater than a set threshold value in the N classifications;

determining a corresponding new video label according to the video data in the classification with the maximum change rate in the screened classifications;

and updating the labeled video tags by using the new video tags, and selecting M video data from the classification with the maximum change rate to update the tagged video data.

According to a third aspect of the embodiments of the present invention, there is provided an electronic device for dynamically updating a video tag, including: a memory and a processor;

wherein the memory is used for storing programs;

the processor is used for executing the program in the memory and comprises the following steps:

receiving video data to be labeled uploaded by a platform account, and acquiring buffered video data outside a label video data set from the video data uploaded by the platform account before, wherein the label video data set comprises at least one label video data of a label of a labeled video;

classifying the video data to be marked and the buffered video data by adopting a classification algorithm, and determining the change rate of each classification relative to a label video data set when determining to trigger the update of a video label according to a classification result;

and determining a new video label corresponding to the classification with the change rate larger than the set threshold value, updating the labeled video label by using the new video label, and updating the labeled video data by using the video data in the classification.

Optionally, the processor classifies the video data to be labeled and the buffered video data by using a classification algorithm, including:

inputting the video data to be marked and the buffer video data into a characteristic data extraction model, and respectively extracting characteristic data of the video data to be marked and the buffer video data;

and taking the extracted characteristic data as a sample set, and clustering the extracted characteristic data by adopting a clustering method.

Optionally, the determining, by the processor, to trigger the video tag update according to the classification result includes:

and determining whether to trigger video tag updating according to the proportion of the number of the video data in each category to the total number of the video data, wherein the video data in each category is at least one of the video data to be marked and the buffered video data, and the total number of the video data is the total number of the video data to be marked and the buffered video data.

Optionally, the determining, by the processor, whether to trigger the video tag update according to a ratio of the number of the video data in each category to the total number of the video data includes:

taking the ratio of the number of the video data in the category of the video data to be labeled to the total number of the video data as the probability of triggering the update of the video label, and triggering the update of the video label according to the probability; or

And if the ratio of the number of the video data in the category of the video data to be labeled to the total number of the video data exceeds a set threshold value, triggering the video label to be updated.

Optionally, the processor is specifically configured to:

if the video data uploaded before the platform account does not have the label video data set, sending annotation prompt information, and receiving the fed back video label and the corresponding label video data set, or

If the label video data set does not exist in the video data uploaded before the platform account and the number of the video data exceeds the set number, classifying the uploaded video data, labeling the video labels according to the classification result and determining the corresponding label video data.

Optionally, the obtaining, by the processor, buffered video data outside the tagged video data set from video data previously uploaded by the platform account includes:

and according to the interval between the time of uploading the video data before the platform account and the current time, obtaining the buffered video data outside the tag video data set according to the sequence from small to large of the interval.

Optionally, when determining that the video tag update is triggered, the processor is specifically configured to:

marking the video data to be marked as buffer video data;

and deleting the buffered video data with the largest interval from the current time in the buffered video data.

Optionally, when determining that the video tag update is triggered, the processor determines a change rate of each classification relative to the tag video data set, including:

selecting a classification with the largest number of video data in the classifications, and determining the change rate of the classification relative to the label video data set; or

And selecting N classes according to the sequence of the number of the video data in the classes from most to less, and determining the change rate of each class relative to the label video data set, wherein N is a positive integer greater than 1.

Optionally, the processor determines a rate of change of each classification with respect to the set of tagged video data, including:

selecting M video data according to the sequence of the distances from each video data in the classification to the corresponding classification center from small to large, wherein M is a preset positive integer larger than 1;

removing video data belonging to the same video label as the label video data set from the M video data to obtain a new set;

and dividing the number of the video data in the new set by the number of the label video data in the label video data set to obtain the change rate of the classification relative to the label video data set.

Optionally, the processor selects M pieces of video data according to a sequence from small to large of distances from each piece of video data in the classification to a corresponding classification center, where the selecting includes:

averaging the characteristic data corresponding to the video data in the classification to obtain a classification center;

determining the cosine distance from the feature data corresponding to each video data in the classification to the classification center, and selecting M video data from the classification according to the sequence of the cosine distances from small to large.

Optionally, if the processor selects a category with the largest number of video data in the categories, determining a corresponding new video tag according to the video data in the category with the change rate greater than the set threshold, including:

if the change rate of the classification relative to the label video data set is larger than a set threshold value, determining a corresponding new video label according to the video data in the classification;

and updating the labeled video tags by using the new video tags, and selecting M video data from the classification to update the tagged video data.

Optionally, if the processor selects N categories according to the order of the number of video data in the categories, determining a corresponding new video tag according to the video data in the category of which the change rate is greater than the set threshold, including:

screening out the classification of which the change rate is greater than a set threshold value in the N classifications;

determining a corresponding new video label according to the video data in the classification with the maximum change rate in the screened classifications;

and updating the labeled video tags by using the new video tags, and selecting M video data from the classification with the maximum change rate to update the tagged video data.

According to a fourth aspect of the embodiments of the present invention, there is provided a chip, which is coupled to a memory in an electronic device, so that when the chip is executed, program instructions stored in the memory are called, so as to implement the above aspects of the embodiments of the present application and any designed method related to the aspects.

According to a fifth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing program instructions which, when executed on a computer, cause the computer to perform the method of any of the possible designs to which the above aspects and aspects relate.

According to a sixth aspect of the embodiments of the present invention, there is provided a computer program product, which, when run on an electronic device, causes the electronic device to perform a method of implementing the above aspects of the embodiments of the present application and any possible design related to the aspects.

The method, the device and the electronic equipment for dynamically updating the video tag have the following beneficial effects that:

the method, the device and the electronic equipment for dynamically updating the video tags, provided by the invention, classify newly uploaded video data to be annotated of a platform account and previously uploaded buffer video data by utilizing a classification method, determine the change rate of each classification relative to a tag video data set according to a classification result, update a tag video data set when the change rate reaches a certain value, and solve the problems of representative data perception and untimely update of the platform account in the conventional method for determining the video tags.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic diagram illustrating a method for dynamically updating a video tag according to an embodiment of the present invention;

fig. 2 is a flowchart illustrating a method for dynamically updating a video tag according to an embodiment of the present invention;

fig. 3 is a schematic diagram of an apparatus for dynamically updating video tags according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device for dynamically updating a video tag according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.

The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like. The method for playing the voice message provided by the embodiment of the invention applies an artificial intelligence technology, and for convenience of understanding, terms related in the embodiment of the invention are explained as follows:

1) machine Learning (ML): the method is a multi-field cross discipline and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. Specially researching how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer; machine learning is the core of artificial intelligence, is a fundamental approach for enabling computers to have intelligence, and is applied to all fields of artificial intelligence; machine learning and deep learning generally comprise technologies such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, formula teaching learning and the like;

2) UGC (User Generated Content): the method comprises the steps that original content of a user is shown or provided for other users through an internet platform; UGC is not a specific service, but a new way for users to use the Internet, namely, the original downloading is changed into the main downloading and uploading, the interaction of network users is reflected along with the development of Internet application, and the users are not only network content browsers but also network content creators;

3) user portrait: the user portrait is a virtual representation of a real platform account, each piece of concrete information of the platform account is abstracted into a label, and the label is utilized to embody the image of the platform account, so that targeted service is provided for the platform account;

4) clustering: clustering refers to the process of dividing a collection of physical or abstract objects into classes composed of similar objects; generated by the clustering is a set of data objects that are similar to objects in the same cluster and distinct from objects in other clusters; common clustering methods include a system clustering method, an ordered sample clustering method, a dynamic clustering method, a fuzzy clustering method, a graph theory clustering method, a clustering prediction method and the like, and common clustering algorithms include a K-means clustering algorithm, a mean shift clustering algorithm, a DBSCAN clustering algorithm, an expectation maximization clustering algorithm using a Gaussian mixture model, a hierarchical clustering algorithm and the like;

5) DBSCAN Clustering algorithm (Density-Based Spatial Clustering of applied switching Noise, Density-Based Noise application Spatial Clustering): the method is a density-based clustering algorithm, which defines clusters as the maximum set of points connected by density, can divide areas with high enough density into clusters, and can find clusters with any shapes in a noise spatial database.