WO2023000782A1 - Procédé et appareil d'acquisition d'un point d'accès sans fil vidéo, support lisible, et dispositif électronique - Google Patents

Procédé et appareil d'acquisition d'un point d'accès sans fil vidéo, support lisible, et dispositif électronique Download PDF

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WO2023000782A1
WO2023000782A1 PCT/CN2022/092514 CN2022092514W WO2023000782A1 WO 2023000782 A1 WO2023000782 A1 WO 2023000782A1 CN 2022092514 W CN2022092514 W CN 2022092514W WO 2023000782 A1 WO2023000782 A1 WO 2023000782A1
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text
cluster
texts
video
clustering
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PCT/CN2022/092514
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English (en)
Chinese (zh)
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佘琪
沈铮阳
王长虎
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北京有竹居网络技术有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • 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

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  • the present disclosure relates to the technical field of the Internet, and in particular, to a method, device, readable medium and electronic equipment for acquiring video hotspots.
  • video hotspots are mainly obtained through manual summarization or hotspot discovery models (eg, latent Dirichlet model or latent semantic analysis model).
  • hotspot discovery models eg, latent Dirichlet model or latent semantic analysis model.
  • using artificial summarization to obtain video hotspots will consume a lot of human resources as the data flow continues to increase, and the efficiency is low and the real-time performance is poor.
  • using the hotspot mining model to obtain video hotspots as the amount of data increases, the calculation cost is high, and ambiguous expressions are prone to occur, which reduces the accuracy of the acquired video hotspots.
  • the present disclosure provides a method for acquiring video hotspots, the method comprising:
  • For each of the first text clusters determine a second preset classification number corresponding to the first text cluster, and cluster the texts in the first text cluster according to the second preset classification number , obtaining the second text clusters of the second preset classification quantity;
  • a video hotspot corresponding to the at least one video page is determined according to the cluster center of each second text cluster.
  • the present disclosure provides a device for acquiring video hotspots, the device comprising:
  • An acquisition module configured to identify the page information of at least one video page, and obtain multiple texts corresponding to the at least one video page;
  • the first clustering module is used to cluster a plurality of said texts to obtain the first text clusters of the first preset classification quantity;
  • the second clustering module is configured to, for each of the first text clusters, determine a second preset classification number corresponding to the first text cluster, and classify the first text according to the second preset classification number
  • the texts in the clustering are clustered to obtain the second preset classification number of second text clusters;
  • the determination module is configured to determine the video hotspot corresponding to the at least one video page according to the cluster center of each of the second text clusters.
  • the present disclosure provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the steps of the method described in the first aspect of the present disclosure are implemented.
  • an electronic device including:
  • a processing device configured to execute the computer program in the storage device to implement the steps of the method described in the first aspect of the present disclosure.
  • the present disclosure first identifies the page information of at least one video page to obtain multiple texts corresponding to at least one video page, and then clusters the multiple texts to obtain the first preset classification number of first texts Clustering, and then for each first text cluster, determine the second preset classification number corresponding to the first text cluster, and cluster the text in the first text cluster according to the second preset classification number , to obtain a second preset classification number of second text clusters, and finally determine a video hotspot corresponding to at least one video page according to the cluster center of each second text cluster.
  • Fig. 1 is a flow chart showing a method for acquiring video hotspots according to an exemplary embodiment
  • Fig. 2 is a flow chart showing a step 102 according to the embodiment shown in Fig. 1;
  • Fig. 3 is a flow chart showing a step 103 according to the embodiment shown in Fig. 1;
  • Fig. 4 is a block diagram of a device for acquiring video hotspots according to an exemplary embodiment
  • Fig. 5 is a block diagram showing a first clustering module according to the embodiment shown in Fig. 4;
  • Fig. 6 is a block diagram showing a second clustering module according to the embodiment shown in Fig. 4;
  • Fig. 7 is a block diagram of an acquisition module according to the embodiment shown in Fig. 4;
  • Fig. 8 is a block diagram of an electronic device according to an exemplary embodiment.
  • Fig. 1 is a flow chart of a method for acquiring video hotspots according to an exemplary embodiment. As shown in Figure 1, the method may include the following steps:
  • Step 101 Identify the page information of at least one video page to obtain multiple texts corresponding to the at least one video page.
  • the manner of recognizing the page information of at least one video page to obtain multiple texts may be: performing text recognition on the text information of each video page to obtain the page text corresponding to each video page.
  • audio recognition can be performed on the audio information of each video page to obtain the audio text corresponding to each video page.
  • the page text and the audio text can be used as multiple texts.
  • OCR English: Optical Character Recognition, Chinese: Optical Character Recognition
  • existing video data can be used to identify each Text recognition of live room titles, live introduction texts, live comments, live barrage and other texts of each live page to obtain the page text corresponding to each live page.
  • Step 102 clustering a plurality of texts to obtain a first preset number of first text clusters.
  • the first preset classification number can be set in advance, and according to the first preset classification number, a preset clustering algorithm is used to cluster a plurality of texts to obtain the first preset classification number of first text clusters.
  • the preset clustering algorithm can be, for example, the K-Means clustering algorithm
  • the first preset classification number can be artificially set according to experience, or can be based on coarse-grained texts in multiple texts (for example, titles, topic introductions, etc.) The words in the corresponding text) are selected.
  • the process of obtaining the number of first text clusters of the first preset classification can actually be understood as a coarse-grained clustering process.
  • the clustering granularity of the first text cluster is relatively coarse, and each first text cluster contains a type of text.
  • the three first text clusters can respectively contain The texts of sports, film and television, and games, that is, the clustering granularity of the first text clustering is at the level of sports, film and television, and games.
  • the second preset classification quantity corresponding to each first text cluster can be determined, and the second preset classification quantity can be a preset fixed value, or can be based on the of the text selected. Then, according to the second preset classification number corresponding to each first text cluster, the text in the first text cluster can be clustered by using a preset clustering algorithm, and the first text cluster corresponding to the first text cluster can be obtained. Two preset classifications and a second text clustering. At this time, the number of second text clusters finally obtained is the sum of the second preset classification numbers corresponding to each first text cluster. Obtaining the second preset number of second text clusters corresponding to each first text cluster can actually be understood as a fine-grained clustering process.
  • the clustering granularity of the second text clustering is relatively fine.
  • the second preset classification number corresponding to the first text clustering can be set to 3
  • the three second text clusters corresponding to the first text cluster can respectively contain track and field, football and basketball texts, that is, the clustering granularity of the second text cluster is at the level of track and field, football and basketball.
  • Step 104 according to the cluster center of each second text cluster, determine the video hotspot corresponding to at least one video page.
  • the first number of texts closest to the cluster center of the second text cluster in the second text cluster can be used as the target text, and the TF-IDF corresponding to each word in the second text cluster The largest second number of words is used as the target word. Then, the target text and target words can be used as video hotspots.
  • video hotspots By selecting video hotspots from the second text clustering, the expression form of video hotspots is clear, which is convenient for subsequent processing and analysis.
  • the method for acquiring video hotspots in the present disclosure can be applied not only to acquiring video and live broadcast hotspots, but also to other types of hotspots. For example, it can be applied to acquiring hotspots in images, and this disclosure does not make any Specific limits.
  • Fig. 2 is a flow chart showing a step 102 according to the embodiment shown in Fig. 1 .
  • step 102 may include the following steps:
  • Step 1021 determine the TF-IDF of each word in the multiple texts.
  • text preprocessing can be performed on multiple texts after acquiring multiple texts, so as to remove information irrelevant to video hotspots (such as punctuation marks) in each text. , stop words, etc.) and sensitive information.
  • word segmentation can be performed on multiple texts that have undergone text preprocessing, and then a vocabulary corresponding to multiple texts can be constructed according to the word segmentation results (the vocabulary corresponding to multiple texts includes all words in multiple texts), and multiple texts corresponding to each other can be calculated.
  • TF-IDF for each word in the vocabulary of .
  • Step 1022 for each text, according to the TF-IDF of each word in the multiple texts and the word vector corresponding to each word in the text, determine the text vector corresponding to the text.
  • Step 1023 According to the first preset number of categories, use a preset clustering algorithm to cluster the text vectors corresponding to the multiple texts to obtain the first preset number of first text clusters.
  • the TF-IDF of each word in the text can be used for weighted average to obtain the text vector corresponding to the text, That is, the text features of the text.
  • the text vectors corresponding to the multiple texts may be clustered by using a preset clustering algorithm to obtain the first preset classification number of first text clusters.
  • step 103 can be implemented in the following manner:
  • the second preset number of categories corresponding to the first text cluster may be determined according to the central sentence and keywords of the first text cluster.
  • the central sentence and keywords of each first text cluster can be fed back to the user, and the user can determine the category of the text contained in the first text cluster according to the central sentence and keywords of the first text cluster, And set the corresponding second preset classification number for the first text cluster according to the category, wherein the central sentence can be several texts closest to the cluster center of the first text cluster in the first text cluster,
  • the keywords may be several words with the largest TF-IDF in the first text clustering.
  • the second preset number of categories corresponding to the first text cluster may be determined according to the number of texts in the first text cluster. Specifically, for the first text cluster with a large number of texts, a larger number of second preset classifications may be set. For example, when the number of first preset classifications is 4, and the number of texts included in the four first text clusters is 100, 10, 20, and 50, the second preset of the first text cluster with the number of texts of 100 can be Set the number of classifications to 5, set the second preset classification number of the first text cluster with 10 texts to 2, and set the second preset classification number of the first text cluster with 20 texts to 3 , set the second preset category number of the first text cluster whose number of texts is 50 to 4.
  • FIG. 3 is a flow chart of step 103 according to the embodiment shown in FIG. 1 .
  • the second preset classification quantity includes multiple, and step 103 may include the following steps:
  • Step 1031 for each second preset classification number, use a preset clustering algorithm to cluster the texts in the first text clustering according to the second preset classification number to obtain the second preset classification number candidate text clusters.
  • Step 1032 according to the candidate text clusters, determine the target preset category number from multiple second preset category numbers.
  • Step 1033 cluster the candidate texts corresponding to the target preset number of categories as second text clusters of the second preset number of categories.
  • each candidate text cluster can be determined by using indicators such as the contour coefficient method, the elbow method, and the CH coefficient (English: Calinski-Harabasz Index)
  • the clustering effect of the set, and the second preset classification number corresponding to the candidate text clustering set with the best clustering effect is used as the target preset classification number.
  • the candidate text clusters in the candidate text cluster set corresponding to the target preset number of categories are used as the second preset number of second text clusters.
  • the disclosure first identifies the page information of at least one video page to obtain multiple texts corresponding to at least one video page, and then clusters the multiple texts to obtain the first preset classification number of first texts Clustering, and then for each first text cluster, determine the second preset classification number corresponding to the first text cluster, and cluster the text in the first text cluster according to the second preset classification number , to obtain a second preset classification number of second text clusters, and finally determine a video hotspot corresponding to at least one video page according to the cluster center of each second text cluster.
  • This disclosure efficiently acquires video hotspots by clustering the text in the video page multiple times, which can ensure the real-time performance of video hotspots, does not require manual participation, has low calculation costs, and can avoid ambiguous expressions , improving the accuracy of acquired video hotspots.
  • Fig. 4 is a block diagram of an apparatus for acquiring video hotspots according to an exemplary embodiment. As shown in Figure 4, the device 200 includes:
  • the first clustering module 202 is configured to cluster multiple texts to obtain a first preset number of first text clusters.
  • Determining module 204 is used for determining the video hotspot corresponding to at least one video page according to the cluster center of each second text cluster.
  • Fig. 5 is a block diagram of a first clustering module according to the embodiment shown in Fig. 4 .
  • the first clustering module 202 includes:
  • the second determination sub-module 2021 is configured to determine the TF-IDF of each word in the multiple texts.
  • the second determination sub-module 2021 is further configured for each text, according to the TF-IDF of each word in the multiple texts and the word vector corresponding to each word in the text, to determine the text vector corresponding to the text.
  • the first clustering sub-module 2022 is further configured to use a preset clustering algorithm to cluster the text vectors corresponding to a plurality of texts according to the first preset classification number to obtain the first preset classification number of first text clusters .
  • the second clustering module 203 is used for:
  • the second clustering module 203 is used for:
  • the second preset classification number corresponding to the first text cluster is determined.
  • the second clustering sub-module 2031 is configured to cluster the texts in the first text clustering using a preset clustering algorithm according to the second preset classification number for each second preset classification number, to obtain There are a number of candidate text clusters for the second preset classification.
  • the third determination sub-module 2032 is configured to determine the target preset number of categories from multiple second preset numbers of categories according to the candidate text clusters.
  • the third determination sub-module 2032 is further configured to cluster candidate texts corresponding to the target preset number of categories as second text clusters of the second preset number of categories.
  • the determination module 204 is used for:
  • each second text cluster according to the distance between each text in the second text cluster and the cluster center of the second text cluster, determine the target text corresponding to the second text cluster, and according to the The TF-IDF corresponding to each word in the second text cluster determines the target word corresponding to the second text cluster.
  • the recognition sub-module 2011 is configured to perform text recognition on the text information of each video page to obtain the page text corresponding to each video page.
  • the identification sub-module 2011 is further configured to perform audio identification on the audio information of each video page to obtain the corresponding audio text of each video page.
  • the disclosure first identifies the page information of at least one video page to obtain multiple texts corresponding to at least one video page, and then clusters the multiple texts to obtain the first preset classification number of first texts Clustering, and then for each first text cluster, determine the second preset classification number corresponding to the first text cluster, and cluster the text in the first text cluster according to the second preset classification number , to obtain a second preset classification number of second text clusters, and finally determine a video hotspot corresponding to at least one video page according to the cluster center of each second text cluster.
  • This disclosure efficiently acquires video hotspots by clustering the text in the video page multiple times, which can ensure the real-time performance of video hotspots, does not require manual participation, has low calculation costs, and can avoid ambiguous expressions , improving the accuracy of acquired video hotspots.
  • embodiments of the present disclosure include a computer program product, which includes a computer program carried on a non-transitory computer readable medium, where the computer program includes program code for executing the method shown in the flowchart.
  • the computer program may be downloaded and installed from a network via communication means 309, or from storage means 308, or from ROM 302.
  • the processing device 301 When the computer program is executed by the processing device 301, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.
  • the above-mentioned computer-readable medium in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two.
  • a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device .
  • Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.
  • the client and the server can communicate using any currently known or future network protocols such as HTTP (HyperText Transfer Protocol, Hypertext Transfer Protocol), and can communicate with digital data in any form or medium Communications (eg, communication networks) are interconnected.
  • Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network of.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.
  • the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: recognizes the page information of at least one video page, and obtains the at least one video page a plurality of corresponding texts; clustering a plurality of the texts to obtain a first preset classification number of first text clusters; for each of the first text clusters, determine the corresponding text of the first text clusters a second preset classification number, and cluster the texts in the first text cluster according to the second preset classification number to obtain the second preset classification number of second text clusters; according to each The cluster center of the second text cluster determines the video hotspot corresponding to the at least one video page.
  • each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.
  • FPGAs Field Programmable Gate Arrays
  • ASICs Application Specific Integrated Circuits
  • ASSPs Application Specific Standard Products
  • SOCs System on Chips
  • CPLD Complex Programmable Logical device
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • a machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium.
  • a machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing.
  • Example 1 provides a method for acquiring video hotspots, including: identifying the page information of at least one video page, and obtaining multiple texts corresponding to the at least one video page; Clustering a plurality of the texts to obtain a first preset classification number of first text clusters; for each of the first text clusters, determining a second preset classification number corresponding to the first text cluster, and clustering the text in the first text clustering according to the second preset classification number to obtain the second preset classification number of second text clusters; according to each of the second text clusters The clustering centers of the at least one video page are determined to determine the video hotspot corresponding to the at least one video page.
  • Example 3 provides the method of Example 1, the determining the second preset classification quantity corresponding to the first text cluster includes: according to the text in the first text cluster , to determine the second preset classification quantity corresponding to the first text cluster.
  • Example 9 provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the steps of the methods described in Example 1 to Example 7 are implemented.
  • Example 10 provides an electronic device, including: a storage device, on which a computer program is stored; a processing device, configured to execute the computer program in the storage device, to Implement the steps of the method described in Example 1 to Example 7.

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Abstract

La présente invention concerne un procédé et un appareil d'acquisition d'un point d'accès sans fil vidéo, un support lisible et un dispositif électronique. Le procédé consiste à : identifier des informations de page d'au moins une page vidéo afin d'obtenir une pluralité de textes ; regrouper la pluralité de textes pour obtenir une première quantité de classification prédéfinie de premiers groupes de textes ; pour chaque premier groupe de textes, déterminer une deuxième quantité de classification prédéfinie correspondant au premier groupe de textes, et regrouper des textes du premier groupe de textes selon la deuxième quantité de classification prédéfinie, afin d'obtenir une deuxième quantité de classification prédéfinie de deuxièmes groupes de textes ; et déterminer, selon un centre de groupe de chaque deuxième groupe de textes, un point d'accès vidéo correspondant à ladite page vidéo.
PCT/CN2022/092514 2021-07-21 2022-05-12 Procédé et appareil d'acquisition d'un point d'accès sans fil vidéo, support lisible, et dispositif électronique WO2023000782A1 (fr)

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CN109299271A (zh) * 2018-10-30 2019-02-01 腾讯科技（深圳）有限公司 训练样本生成、文本数据、舆情事件分类方法及相关设备
CN109739978A (zh) * 2018-12-11 2019-05-10 中科恒运股份有限公司 一种文本聚类方法、文本聚类装置及终端设备
CN109918656A (zh) * 2019-02-28 2019-06-21 武汉斗鱼鱼乐网络科技有限公司 一种直播热点获取方法、装置、服务器及存储介质
CN111460153A (zh) * 2020-03-27 2020-07-28 深圳价值在线信息科技股份有限公司 热点话题提取方法、装置、终端设备及存储介质
CN112925905A (zh) * 2021-01-28 2021-06-08 北京达佳互联信息技术有限公司 提取视频字幕的方法、装置、电子设备和存储介质
CN113420723A (zh) * 2021-07-21 2021-09-21 北京有竹居网络技术有限公司 获取视频热点的方法、装置、可读介质和电子设备

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