WO2017113605A1 - Method and device for channel identification - Google Patents

Abstract

Disclosed in the present invention is a method for channel identification, the method for channel identification comprising the following steps: capturing a plurality of screen images in a preset identification period; extracting an identification image comprising a channel identifier from each of the screen images, respectively; performing characteristic extraction according to all the identification images in the identification period, so as to obtain a characteristic image; and calculating the similarity between each preset library image and the characteristic image, respectively, and taking the channel corresponding to the library image with the highest similarity as the channel locked by a user in the identification period. Further disclosed in the present invention is a device for channel identification. By means of the present invention, the calculation amount for extracting the characteristic image is reduced, the operation efficiency is improved and meanwhile, only small identification images are processed, reducing the complexity of the images, improving the identification accuracy. In addition, the processing is performed by means of the captured screen images and the pre-stored library images, without for the necessity of interaction with a server at all times, further improving the operation efficiency, saving the cost.

Description

Channel identification method and device Technical field

The present invention relates to the field of smart televisions, and in particular, to a channel identification method and apparatus.

Background technique

At present, with the development of electronic technology, the use of smart TVs is becoming wider and wider, and the channels that users can watch are also increasing. In order to facilitate the manufacturer to understand the user's habits of using the smart TV and the user's preference for the television program, the television content of the user's preference is provided in a targeted manner. Smart TVs often require channel identification and statistics on the TV channels that users watch. In the existing channel statistics process, it is necessary to continuously interact with the server to determine the current channel information. Since the interaction process with the remote server often has a delay time, the problem of inaccurate recognition and slow operation is often caused.

Therefore, in the existing channel recognition process, it is necessary to interact with the server at all times, and the operation speed is slow and the recognition accuracy is low. The problem in this regard is urgently solved by the inventors.

Summary of the invention

The main object of the present invention is to solve the problem that the existing channel identification process needs to interact with the server at all times, and the operation speed is slow and the recognition accuracy is low.

To achieve the above object, the present invention provides a channel identification method, the channel identification method comprising the following steps:

Acquiring multiple screen images during a preset recognition period;

Performing each of the screen images into a sixteen-square grid to obtain a corresponding area image group;

Extracting an area image of the first row and the first column of each of the regional image groups as a corresponding identification image;

Calculating an average matrix according to a matrix corresponding to each of the identification images;

Calculating a distance matrix of the matrix corresponding to each of the identifier images and the mean matrix;

Performing feature decomposition on each of the distance matrices to obtain a corresponding feature vector matrix;

Calculating a matrix of entropy values corresponding to each of the feature vector matrices;

Calculating an average of all the entropy matrixes to obtain a difference matrix;

The points in the difference matrix are binarized, and the obtained binarized image is used as a feature image;

The similarity between the preset library images and the feature image is separately calculated, and the channel corresponding to the library image with the highest similarity is used as the channel locked by the user in the recognition period.

To achieve the above object, the present invention provides a channel identification method, the channel identification method comprising the following steps:

Acquiring multiple screen images during a preset recognition period;

Extracting an identification image containing a channel identifier from each of the screen images;

Performing feature extraction according to all the identification images in the recognition period to obtain a feature image;

The similarity between the preset library images and the feature image is separately calculated, and the channel corresponding to the library image with the highest similarity is used as the channel locked by the user in the recognition period.

Preferably, the step of performing feature extraction according to all the identification images in the identification period to obtain the feature image comprises:

Calculating an average matrix according to a matrix corresponding to each of the identification images;

Calculating a distance matrix of the matrix corresponding to each of the identifier images and the mean matrix;

Performing feature decomposition on each of the distance matrices to obtain a corresponding feature vector matrix;

Calculating a matrix of entropy values corresponding to each of the feature vector matrices;

Calculating an average of all the entropy matrixes to obtain a difference matrix;

The points in the difference matrix are binarized, and the obtained binarized image is used as the feature image.

In addition, in order to achieve the above object, the present invention further provides a channel identification apparatus, where the channel identification apparatus includes:

An acquisition module, configured to collect multiple screen images in a preset recognition period;

An identifier extraction module, configured to respectively extract an identifier image containing a channel identifier from each of the screen images;

a feature extraction module, configured to perform feature extraction according to all the identification images in the recognition period to obtain a feature image;

a determining module, configured to separately calculate a similarity between the preset library images and the feature image, and use a channel corresponding to the library image with the highest similarity as a frequency locked by the user in the identification period Road.

The invention collects the screen image by timing, and extracts the identification image containing the channel identifier based on the collected screen image, and extracts the feature image based on the smaller identification image, thereby greatly reducing the calculation amount of the extracted feature image and improving the operation efficiency. At the same time, only the small logo image is processed, which reduces the complexity of the image and improves the accuracy of the recognition. Moreover, by collecting the screen image and the pre-stored library image for processing, there is no need to interact with the server at all times, which further improves the operation efficiency and saves the cost.

DRAWINGS

1 is a schematic flow chart of a first embodiment of a channel identification method according to the present invention;

2 is a schematic flow chart of a second embodiment of a channel identification method according to the present invention;

3 is a schematic flow chart of a third embodiment of a channel identification method according to the present invention;

4 is a schematic flow chart of a fourth embodiment of a channel identification method according to the present invention;

FIG. 5 is a schematic diagram of functional modules of a first embodiment of a channel identification apparatus according to the present invention; FIG.

6 is a schematic diagram of functional modules of a second embodiment of a channel identification apparatus according to the present invention;

The implementation, functional features, and advantages of the present invention will be further described in conjunction with the embodiments.

detailed description

It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The invention provides a channel identification method.

Referring to FIG. 1, FIG. 1 is a schematic flowchart diagram of a first embodiment of a channel identification method according to the present invention.

In this embodiment, the channel identification method includes:

Step S10, collecting a plurality of screen images in a preset recognition period;

The smart TV collects a plurality of screen images in a preset recognition period to analyze and determine a channel locked by the user during the recognition period. Specifically, the smart TV can collect the screen image with a preset sampling period; the smart TV summarizes the collected screen images according to the recognition period for analysis. Preferably, the sampling period can be set to 5 minutes, and the recognition period can be set to 1 hour, for example, smart TV every 5 The screen image was collected once every minute, 60/5=12 screen images were collected in the 1 hour recognition period, and the 12 screen images were analyzed as samples to determine the channel locked by the user within 1 hour.

The process of collecting screen images by smart TVs can be achieved by screen capture. Specifically, the smart TV can obtain the screen image through the screen capture; the smart TV determines whether the captured image resolution conforms to the preset reference sample specification; if the reference sample specification is met, the captured image is saved as the sample image; For the reference sample size, the captured image is interpolated to obtain an image conforming to the reference sample size, and the image obtained by the interpolation process is saved as a sample image.

The reference sample specification makes the channel identification method of the present invention applicable to different models. Preferably, the resolution and aspect ratio of 1920*1080 can be selected as the reference sample specification, and the data can be selected to not only take care of the high-end machine of 4K resolution. Type, it can also be taken care of for low-end models. For example, for a model with a resolution of 1920*1080, the screen capture specification is 1920*1080, then the direct screen capture is used as the sample image; for models other than 1920*1080, the image of the screen capture image needs to be interpolated to convert to 1920*1080. Specifications of the image.

Further, in order to improve the processing efficiency, the smart TV can read the resolution of the screen before collecting the screen image, and determine whether the read screen resolution conforms to the preset reference sample specification, and if the screen resolution conforms to the benchmark sample specification, The screen image is directly captured as a sample image; if the screen resolution does not conform to the reference sample specification, the interpolation process is performed each time the screen image is captured, and an image conforming to the reference sample specification is obtained for storage. It is not necessary to perform a judgment on whether or not the reference sample specification is met every time the screen image is captured, thereby improving the processing efficiency.

Step S20, extracting a logo image containing a channel identifier from each screen image;

The smart TV separately extracts the identification image of each screen image to obtain a corresponding plurality of identification images. Preferably, the screen image is an image that conforms to a benchmark sample size. The smart television can extract the identification image from the determined area by determining an area containing the channel identification in the screen image.

Step S30, performing feature extraction according to all the identification images in the identification period to obtain a feature image;

The smart TV extracts features according to all the identification images in the recognition period, and extracts feature images containing feature points from the plurality of identification images, and the feature points are points at which the identification images are fixed in the recognition period. Preferably, the smart television extracts the feature image extracted according to the plurality of identification images into one, that is, the channel locked by the user determined within one identification period is one.

Step S40, respectively calculating the similarity between the preset library images and the feature image, and using the channel corresponding to the library image with the highest similarity as the channel locked by the user in the recognition period.

The smart TV separately calculates the similarity between the preset library images and the feature images; the smart TV uses the channel corresponding to the library image with the highest similarity as the channel locked by the user in the recognition period. Specifically, the smart TV reads the preset library image, calculates the similarity between each library image and the feature image, determines the library image with the highest similarity, reads the channel information corresponding to the determined library image, and determines the user according to the channel information. The channel that is locked during the recognition period.

Further, before the step S10, the smart TV may further load the preset library image and the corresponding channel information to perform channel identification according to the library image and the corresponding channel information. Alternatively, in another embodiment of the present invention, the smart television may also acquire the library image and the corresponding channel information from the server in the channel identification process.

It should be understood that if the user does not switch channels during an identification period, the collected screen images are all one channel, and the calculation accuracy is the highest at this time; if the user switches channels a small amount in one recognition period and is collected to the screen. If the sample sampled by one of the channels A reaches a preset ratio or more, the algorithm has the fault tolerance mechanism, and the channel A can still be calculated; if the user frequently changes the station within one recognition period, and the number of sampling times of one channel does not exceed The preset number of times, this calculation loses meaning, the situation is consistent with reality, users frequently change channels quickly, and they do not have statistical characteristics, such cases are not considered.

In this embodiment, the screen image is collected periodically, and the identifier image containing the channel identifier is extracted based on the collected screen image, and the feature image is extracted based on the smaller logo image, thereby greatly reducing the calculation amount of the extracted feature image and improving the operation efficiency. At the same time, only the small logo image is processed, which reduces the complexity of the image and improves the accuracy of the recognition. Moreover, by collecting the screen image and the pre-stored library image for processing, there is no need to interact with the server at all times, which further improves the operation efficiency and saves the cost.

Referring to FIG. 2, FIG. 2 is a schematic flowchart diagram of a second embodiment of a channel identification method according to the present invention. Based on the first embodiment of the above channel identification method, step S30 includes:

Step S31, calculating an average matrix according to a matrix corresponding to each identifier image;

The smart television can generate a corresponding matrix C _i (matrix corresponding to the i-th identification image) according to each identification image, and calculate an average matrix C _mean based on the generated respective matrices C _i . For example, if the resolution of the identification image is 480*270, the corresponding matrix C _i scale is 480*270.

Alternatively, the smart television may generate a corresponding matrix S _i (matrix corresponding to the i-th screen image) according to each screen image, and separately segment each matrix S _i to obtain a corresponding matrix C _i . For example, if the resolution of the screen image is 1920*1080, the obtained matrix S _{i has} a specification of 1920*1080, and the matrix C _i obtained by performing the sixteen-square grid cutting according to the matrix S _i is 480*270.

Specifically, taking 12 identification images of an identification period as an example, the smart television calculates a mean matrix C _mean according to each matrix C _i , wherein:

Step S32, respectively calculating a distance matrix of the matrix corresponding to each identifier image and the mean matrix;

Smart TV image respectively calculated for each identifier corresponding to the distance matrix and the matrix C _i D _i C _mean mean matrix (matrix C _i corresponding to the distance matrix).

Specifically, taking 12 identification images as an example, the smart television calculates the distance matrix D _{i of} each matrix C _i and the mean matrix C _mean to obtain a distance matrix set D, where:

D=[C ₁ -C _mean , C ₂ -C _mean ,...C _i -C _mean ,...,C ₁₂ -C _mean ],i∈(1,12)

Step S33, performing feature decomposition on each distance matrix to obtain a corresponding feature vector matrix;

The smart television separately decomposes each distance matrix D _i to obtain a corresponding feature vector matrix V _i (a feature vector matrix corresponding to the distance matrix D _i ).

Specifically, taking 12 identification images as an example, the smart television separately decomposes each distance matrix D _i to obtain a corresponding feature vector matrix V _i and obtains a feature feature vector matrix set V, wherein:

V=[V ₁ , V ₂ ,...,V _i ,...,V ₁₂ ],i∈(1,12)

Step S34, calculating an entropy matrix corresponding to each feature vector matrix;

Smart TV calculated for each feature vector matrix V _i corresponding to the entropy of the matrix E _i (V _i eigenvectors corresponding entropy matrix), wherein:

Step S35, calculating an average value of all entropy value matrices to obtain a difference matrix;

The smart TV calculates the average of all entropy matrix E _i to obtain the difference matrix E _mean . Specifically, taking the 12 entropy matrix E _i as an example, the difference matrix E _{mean is} calculated, where:

In step S36, the points in the difference matrix are binarized, and the obtained binarized image is used as the feature image.

The smart television binarizes the points in the difference matrix E _mean and uses the obtained binarized image as the feature image. Specifically, the smart TV can sort the points in the difference matrix E _mean by the size, the last 10,000 points are marked as white, and the remaining points are marked as black, and the corresponding binarized image is obtained. The binarization process can effectively distinguish the channel identification from the background.

In this embodiment, a feature image containing a feature point is obtained by extracting a fixed feature point in the identification area within the recognition period from the identification image containing the channel identifier. The extracted feature image contains more information related to the channel identification, which improves the calculation accuracy. Moreover, by comparing the feature image and the library image to determine the channel locked by the user, it is not necessary to interact with the server at any time, thereby further improving the operation efficiency and saving the cost.

Referring to FIG. 3, FIG. 3 is a schematic flowchart diagram of a third embodiment of a channel identification method according to the present invention. Based on the first embodiment of the above channel identification method, step S20 includes:

Step S21, each screen image is separately cut into sixteen squares to obtain a corresponding area image group;

Step S22, extracting an area image of the first row and the first column of each area image group as a corresponding identification image.

The smart TV divides each screen image into a corresponding 16-square grid to obtain a corresponding area image group; extracts an area image of the first row and the first column of each area image group as a corresponding identification image, and obtains a logo image corresponding to each screen image. To extract the feature image based on the obtained logo image.

Preferably, smart television may also generate the corresponding matrix S _i, respectively, each of the smart TV matrix S _i for sixteen grids obtained by cutting each of the corresponding sub-matrix based on a screen image; a first row and first column of sub smart TV extraction identity matrix as the matrix C _i S _i of the image corresponding to the matrix. For example, if the resolution of the screen image is 1920*1080, the obtained matrix S _{i has} a specification of 1920*1080, and the matrix C _i obtained by performing the sixteen-square grid cutting according to the matrix S _i is 480*270.

In this embodiment, the screen image is collected periodically, and the identifier image containing the channel identifier is extracted based on the collected screen image, and the feature image is extracted based on the smaller logo image, thereby greatly reducing the calculation amount of the extracted feature image and improving the operation efficiency. At the same time, only the small logo image is processed, which reduces the complexity of the image and improves the accuracy of the recognition.

Referring to FIG. 4, FIG. 4 is a schematic flowchart diagram of a fourth embodiment of a channel identification method according to the present invention. Based on the first embodiment of the channel identification method, after step S40, the method further includes:

Step S51, performing statistics on channels locked by the user in each recognition period within a preset time period;

In step S52, the channel with the preset number of times of the number of locks in the statistical result is used as the preferred channel of the user within the preset time.

The smart TV counts the channels locked by the user in each recognition period in the preset time; the channel in which the number of locks is ranked in the preset number of times in the statistical result is used as the preferred channel of the user within the preset time. The preset number of bits can be set according to the actual settings. For example, it can be set to the top three or the top ten. Further, the smart TV can also upload the top three channels in the statistical results to the server. In order to understand the user's usage habits of the smart TV and the user's preference for the television program through the server, the television content of the user's preference is provided in a targeted manner. The preset time can be 24 hours, 48 hours, and the like.

In this embodiment, by referring to the channels that the user locks in the preset time, the top three channels are used as the preferred channels of the user in the preset time, so that the service provider can understand the user's usage habits of the smart TV and the user preference television programs. Therefore, the television content of the user's preference is provided in a targeted manner.

The execution bodies of the channel identification methods of the above-described first to fourth embodiments may each be a smart TV or an intelligent terminal for playing a television program. Further, the channel identification method may be implemented by a client program installed on a smart TV or a smart terminal for playing a television program, wherein the smart terminal may include, but is not limited to, a mobile phone, a smart phone, a notebook computer, a PDA. Terminals (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), desktop computers, and the like.

The invention further provides a channel identification device.

Referring to FIG. 5, FIG. 5 is a schematic diagram of functional modules of a first embodiment of a channel identification apparatus according to the present invention.

In this embodiment, the channel identification device includes: an acquisition module 10, an identifier extraction module 20, a feature extraction module 30, and a determination module 40.

The collecting module 10 is configured to collect a plurality of screen images in a preset recognition period;

The smart TV collects a plurality of screen images in a preset recognition period to analyze and determine a channel locked by the user during the recognition period. Specifically, the smart TV can collect the screen image with a preset sampling period; the smart TV summarizes the collected screen images according to the recognition period for analysis. Preferably, the sampling period can be set to 5 minutes, and the recognition period can be set to 1 hour. For example, the smart TV collects the screen image every 5 minutes, and collects 60/5=12 screen images in the 1 hour recognition period. The 12 screen images are analyzed as samples to determine the channel that the user has locked within 1 hour.

The process of collecting screen images by smart TVs can be achieved by screen capture. Specifically, the smart TV can obtain the screen image through the screen capture; the smart TV determines whether the resolution of the captured image conforms to the preset. The reference sample specification; if the reference sample specification is met, the captured image is saved as a sample image; if the reference sample specification is not met, the intercepted image is interpolated to obtain an image conforming to the reference sample specification, and the interpolation is processed. The resulting image is saved as a sample image.

The reference sample specification makes the channel identification method of the present invention applicable to different models. Preferably, the resolution and aspect ratio of 1920*1080 can be selected as the reference sample specification, and the data can be selected to not only take care of the high-end machine of 4K resolution. Type, it can also be taken care of for low-end models. For example, for a model with a resolution of 1920*1080, the screen capture specification is 1920*1080, then the direct screen capture is used as the sample image; for models other than 1920*1080, the image of the screen capture image needs to be interpolated to convert to 1920*1080. Specifications of the image.

Further, in order to improve the processing efficiency, the smart TV can read the resolution of the screen before collecting the screen image, and determine whether the read screen resolution conforms to the preset reference sample specification, and if the screen resolution conforms to the benchmark sample specification, The screen image is directly captured as a sample image; if the screen resolution does not conform to the reference sample specification, the interpolation process is performed each time the screen image is captured, and an image conforming to the reference sample specification is obtained for storage. It is not necessary to perform a judgment on whether or not the reference sample specification is met every time the screen image is captured, thereby improving the processing efficiency.

The identifier extraction module 20 is configured to respectively extract the identifier image containing the channel identifier from each screen image;

The smart TV separately extracts the identification image of each screen image to obtain a corresponding plurality of identification images. Preferably, the screen image is an image that conforms to a benchmark sample size. The smart television can extract the identification image from the determined area by determining an area containing the channel identification in the screen image.

The feature extraction module 30 is configured to perform feature extraction according to all the identification images in the recognition period to obtain a feature image;

The smart TV extracts features according to all the identification images in the recognition period, and extracts feature images containing feature points from the plurality of identification images, and the feature points are points at which the identification images are fixed in the recognition period. Preferably, the smart television extracts the feature image extracted according to the plurality of identification images into one, that is, the channel locked by the user determined within one identification period is one.

The determining module 40 is configured to separately calculate the similarity between the preset library images and the feature image, and use the channel corresponding to the library image with the highest similarity as the channel locked by the user in the recognition period.

The smart TV separately calculates the similarity between the preset library images and the feature images; the smart TV uses the channel corresponding to the library image with the highest similarity as the channel locked by the user in the recognition period. specific, The smart TV reads the preset library image, calculates the similarity between each library image and the feature image, determines the library image with the highest similarity, reads the channel information corresponding to the determined library image, and determines the user in the recognition cycle according to the channel information. Internal locked channel.

Further, the channel identification device may further include an initialization module, and the initialization module is configured to load the preset library image and the corresponding channel information to perform channel identification according to the library image and the corresponding channel information.

The smart TV can load the preset library image and the corresponding channel information through the initialization module to perform channel identification according to the library image and the corresponding channel information. Alternatively, in another embodiment of the present invention, the smart television may also acquire the library image and the corresponding channel information from the server in the channel identification process.

It should be understood that if the user does not switch channels during an identification period, the collected screen images are all one channel, and the calculation accuracy is the highest at this time; if the user switches channels a small amount in one recognition period and is collected to the screen. If the sample sampled by one of the channels A reaches a preset ratio or more, the algorithm has the fault tolerance mechanism, and the channel A can still be calculated; if the user frequently changes the station within one recognition period, and the number of sampling times of one channel does not exceed The preset number of times, this calculation loses meaning, the situation is consistent with reality, users frequently change channels quickly, and they do not have statistical characteristics, such cases are not considered.

In this embodiment, the screen image is collected periodically, and the identifier image containing the channel identifier is extracted based on the collected screen image, and the feature image is extracted based on the smaller logo image, thereby greatly reducing the calculation amount of the extracted feature image and improving the operation efficiency. At the same time, only the small logo image is processed, which reduces the complexity of the image and improves the accuracy of the recognition. Moreover, by collecting the screen image and the pre-stored library image for processing, there is no need to interact with the server at all times, which further improves the operation efficiency and saves the cost.

Referring to FIG. 6, FIG. 6 is a schematic diagram of functional modules of a second embodiment of the apparatus of the present invention. Based on the first embodiment of the above channel identification device, the feature extraction module 30 includes a calculation unit 31 and a binarization unit 32.

The calculating unit 31 is configured to calculate an average matrix according to a matrix corresponding to each identifier image;

The smart television can generate a corresponding matrix C _i (matrix corresponding to the i-th identification image) according to each identification image, and calculate an average matrix C _mean based on the generated respective matrices C _i . For example, if the resolution of the identification image is 480*270, the corresponding matrix C _i scale is 480*270.

Alternatively, smart television may be the logo image extraction process, generates a corresponding matrix S _i (i-th screen image corresponding to a matrix) in accordance with various screen images, respectively, each of the matrix S _i dividing process to obtain the corresponding matrix C _i. For example, if the resolution of the screen image is 1920*1080, the obtained matrix S _{i has} a specification of 1920*1080, and the matrix C _i obtained by performing the sixteen-square grid cutting according to the matrix S _i is 480*270.

Specifically, taking 12 identification images of an identification period as an example, the smart television calculates a mean matrix C _mean according to each matrix C _i , wherein:

The calculating unit 31 is further configured to separately calculate a distance matrix of the matrix corresponding to each identifier image and the mean matrix;

Smart TV image respectively calculated for each identifier corresponding to the distance matrix and the matrix C _i D _i C _mean mean matrix (matrix C _i corresponding to the distance matrix).

Specifically, taking 12 identification images as an example, the smart television calculates the distance matrix D _{i of} each matrix C _i and the mean matrix C _mean to obtain a distance matrix set D, where:

D=[C ₁ -C _mean , C ₂ -C _mean ,...C _i -C _mean ,...,C ₁₂ -C _mean ],i∈(1,12)

The calculating unit 31 is further configured to perform feature decomposition on each distance matrix to obtain a corresponding feature vector matrix;

The smart television separately decomposes each distance matrix D _i to obtain a corresponding feature vector matrix V _i (a feature vector matrix corresponding to the distance matrix D _i ).

Specifically, taking 12 identification images as an example, the smart television separately decomposes each distance matrix D _i to obtain a corresponding feature vector matrix V _i and obtains a feature feature vector matrix set V, wherein:

V=[V ₁ , V ₂ ,...,V _i ,...,V ₁₂ ],i∈(1,12)

The calculating unit 31 is further configured to calculate a matrix of entropy values corresponding to each feature vector matrix;

Smart TV calculated for each feature vector matrix V _i corresponding to the entropy of the matrix E _i (V _i eigenvectors corresponding entropy matrix), wherein:

The calculating unit 31 is further configured to calculate an average value of all entropy value matrices to obtain a difference matrix;

The smart TV calculates the average of all entropy matrix E _i to obtain the difference matrix E _mean . Specifically, taking the 12 entropy matrix E _i as an example, the difference matrix E _{mean is} calculated, where:

The binarization unit 32 is configured to perform binarization processing on the points in the difference matrix, and use the obtained binarized image as the feature image.

The smart television binarizes the points in the difference matrix E _mean and uses the obtained binarized image as the feature image. Specifically, the smart TV can sort the points in the difference matrix E _mean by the size, the last 10,000 points are marked as white, and the remaining points are marked as black, and the corresponding binarized image is obtained. The binarization process can effectively distinguish the channel identification from the background.

In this embodiment, a feature image containing a feature point is obtained by extracting a fixed feature point in the identification area within the recognition period from the identification image containing the channel identifier. The extracted feature image contains more information related to the channel identification, which improves the calculation accuracy. Moreover, by comparing the feature image and the library image to determine the channel locked by the user, it is not necessary to interact with the server at any time, thereby further improving the operation efficiency and saving the cost.

Based on the first embodiment of the above channel identification device, the identification extraction module 20 includes a cutting unit 21 and an extraction unit 22;

a cutting unit 21, configured to perform a sixteen-square grid cut on each screen image to obtain a corresponding area image group;

The extracting unit 22 is configured to extract an area image of the first row and the first column of each regional image group as a corresponding identification image.

The smart TV divides each screen image into a corresponding 16-square grid to obtain a corresponding area image group; extracts an area image of the first row and the first column of each area image group as a corresponding identification image, and obtains a logo image corresponding to each screen image. To extract the feature image based on the obtained logo image.

Preferably, smart television may also generate the corresponding matrix S _i, respectively, each of the smart TV matrix S _i for sixteen grids obtained by cutting each of the corresponding sub-matrix based on a screen image; a first row and first column of sub smart TV extraction identity matrix as the matrix C _i S _i of the image corresponding to the matrix. For example, if the resolution of the screen image is 1920*1080, the obtained matrix S _{i has} a specification of 1920*1080, and the matrix C _i obtained by performing the sixteen-square grid cutting according to the matrix S _i is 480*270.

In this embodiment, the screen image is collected periodically, and the identifier image containing the channel identifier is extracted based on the collected screen image, and the feature image is extracted based on the smaller logo image, thereby greatly reducing the calculation amount of the extracted feature image and improving the operation efficiency. At the same time, only the small logo image is processed, which reduces the complexity of the image and improves the accuracy of the recognition.

Based on the first embodiment of the channel identification device, the channel identification device further includes a statistics module 50;

The statistics module 50 is configured to perform statistics on channels locked by users in each identification period within a preset time period;

The determining module 40 is further configured to use, as the preferred channel of the user, the channel of the preset number of times of the number of locks in the statistical result.

The smart TV counts the channels locked by the user in each recognition period in the preset time; the channel in which the number of locks is ranked in the preset number of times in the statistical result is used as the preferred channel of the user within the preset time. The preset number of bits can be set according to the actual settings. For example, it can be set to the top three or the top ten. Further, the smart TV can also upload the top three channels in the statistical results to the server. In order to understand the user's usage habits of the smart TV and the user's preference for the television program through the server, the television content of the user's preference is provided in a targeted manner. The preset time can be 24 hours, 48 hours, and the like.

In this embodiment, by referring to the channels that the user locks in the preset time, the top three channels are used as the preferred channels of the user in the preset time, so that the service provider can understand the user's usage habits of the smart TV and the user preference television programs. Therefore, the television content of the user's preference is provided in a targeted manner.

It is to be understood that the term "comprises", "comprising", or any other variants thereof, is intended to encompass a non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes those elements. It also includes other elements that are not explicitly listed, or elements that are inherent to such a process, method, article, or device. An element that is defined by the phrase "comprising a ..." does not exclude the presence of additional equivalent elements in the process, method, item, or device that comprises the element.

The serial numbers of the embodiments of the present invention are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

The above are only the preferred embodiments of the present invention, and are not intended to limit the scope of the invention, and the equivalent structure or equivalent process transformations made by the description of the present invention and the drawings are directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of the present invention.

Claims (20)

1. A channel identification method, characterized in that the channel identification method comprises the following steps:

   Acquiring multiple screen images during a preset recognition period;

   Performing each of the screen images into a sixteen-square grid to obtain a corresponding area image group;

   Extracting an area image of the first row and the first column of each of the regional image groups as a corresponding identification image;

   Calculating an average matrix according to a matrix corresponding to each of the identification images;

   Calculating a distance matrix of the matrix corresponding to each of the identifier images and the mean matrix;

   Performing feature decomposition on each of the distance matrices to obtain a corresponding feature vector matrix;

   Calculating a matrix of entropy values corresponding to each of the feature vector matrices;

   Calculating an average of all the entropy matrixes to obtain a difference matrix;

   Performing binarization processing on the points in the difference matrix, and using the obtained binarized image as a feature image;

   The similarity between the preset library images and the feature image is separately calculated, and the channel corresponding to the library image with the highest similarity is used as the channel locked by the user in the recognition period.
2. The channel identification method according to claim 1, wherein before the step of acquiring a plurality of screen images in a preset recognition period, the method further comprises:

   Loading a preset library image and corresponding channel information to perform channel identification according to the library image and corresponding channel information.
3. The channel identification method according to claim 1, wherein the calculating the similarity between the preset library images and the feature image respectively, and using the channel corresponding to the library image with the highest similarity as the user After the step of identifying the channel locked in the period, the method further includes:

   Statistics on the channels locked by the users in each recognition period within a preset time period;

   The channel in which the number of locks in the statistical result is ranked as the preset number of bits is used as the preferred channel of the user within the preset time.
4. The channel identification method according to claim 1, wherein said screen image is cut by Before the step of acquiring a plurality of screen images in a preset recognition period, the method further includes:

   Reading the resolution of the screen, determining whether the read screen resolution meets a preset reference sample specification, and if the screen resolution meets the reference sample specification, acquiring multiple screen images in a preset recognition period ;

   If the screen resolution does not conform to the reference sample specification, interpolation processing is performed each time the screen image is captured, and the screen image after the interpolation processing is taken as the collected image.
5. The channel identification method according to claim 1, wherein the step of calculating an average matrix according to a matrix corresponding to each of the identification images comprises:

   In the process of the identification image extraction, a corresponding matrix is generated according to each of the screen images, and the generated matrix is separately subjected to segmentation processing to obtain a matrix corresponding to the identification image, and the mean matrix is calculated based on the matrix of the identification image. .
6. A channel identification method, characterized in that the channel identification method comprises the following steps:

   Acquiring multiple screen images during a preset recognition period;

   Extracting an identification image containing a channel identifier from each of the screen images;

   Performing feature extraction according to all the identification images in the recognition period to obtain a feature image;

   The similarity between the preset library images and the feature image is separately calculated, and the channel corresponding to the library image with the highest similarity is used as the channel locked by the user in the recognition period.
7. The channel identification method according to claim 6, wherein the step of performing feature extraction according to all the identification images in the recognition period to obtain the feature image comprises:

   Calculating an average matrix according to a matrix corresponding to each of the identification images;

   Calculating a distance matrix of the matrix corresponding to each of the identifier images and the mean matrix;

   Performing feature decomposition on each of the distance matrices to obtain a corresponding feature vector matrix;

   Calculating a matrix of entropy values corresponding to each of the feature vector matrices;

   Calculating an average of all the entropy matrixes to obtain a difference matrix;

   The points in the difference matrix are binarized, and the obtained binarized image is used as the feature image.
8. The channel identification method according to claim 7, wherein the calculating the similarity between the preset library images and the feature image respectively, and using the channel corresponding to the library image with the highest similarity as the user After the step of identifying the channel locked in the period, the method further includes:

   Statistics on the channels locked by the users in each recognition period within a preset time period;

   The channel in which the number of locks in the statistical result is ranked as the preset number of bits is used as the preferred channel of the user within the preset time.
9. The channel identification method according to claim 7, wherein the step of calculating an average matrix according to a matrix corresponding to each of the identification images comprises:

   In the process of the identification image extraction, a corresponding matrix is generated according to each of the screen images, and the generated matrix is separately subjected to segmentation processing to obtain a matrix corresponding to the identification image, and the mean matrix is calculated based on the matrix of the identification image. .
10. The channel identification method according to claim 6, wherein the step of extracting the identification image containing the channel identification from each of the screen images respectively comprises:

    Performing each of the screen images into a sixteen-square grid to obtain a corresponding area image group;

    An area image of the first row and the first column of each of the area image groups is extracted as a corresponding identification image.
11. The channel identification method according to claim 6, wherein the step of collecting a plurality of screen images in a preset recognition period further comprises:

    Loading a preset library image and corresponding channel information to perform channel identification according to the library image and corresponding channel information.
12. The channel identification method according to claim 6, wherein the calculating the similarity between the preset library images and the feature image respectively, and using the channel corresponding to the library image with the highest similarity as the user After the step of identifying the channel locked in the period, the method further includes:

    Statistics on the channels locked by the users in each recognition period within a preset time period;

    The channel in which the number of locks in the statistical result is ranked as the preset number of bits is used as the preferred channel of the user within the preset time.
13. The channel identification method according to claim 6, wherein the screen image is obtained by intercepting, and the step of collecting a plurality of screen images in a preset recognition period further comprises:

    Reading the resolution of the screen, determining whether the read screen resolution meets a preset reference sample specification, and if the screen resolution meets the reference sample specification, acquiring multiple screen images in a preset recognition period ;

    If the screen resolution does not conform to the reference sample specification, interpolation processing is performed each time the screen image is captured, and the screen image after the interpolation processing is taken as the collected image.
14. A channel identification device, characterized in that the channel identification device comprises:

    An acquisition module, configured to collect multiple screen images in a preset recognition period;

    An identifier extraction module, configured to respectively extract an identifier image containing a channel identifier from each of the screen images;

    a feature extraction module, configured to perform feature extraction according to all the identification images in the recognition period to obtain a feature image;

    And a determining module, configured to separately calculate a similarity between the preset library images and the feature image, and use a channel corresponding to the library image with the highest similarity as a channel locked by the user in the recognition period.
15. The channel identification device according to claim 14, wherein the feature extraction module comprises a calculation unit and a binarization unit;

    The calculating unit is configured to calculate an average matrix according to a matrix corresponding to each of the identifier images;

    The calculating unit is further configured to separately calculate a distance matrix of the matrix corresponding to each of the identification images and the mean matrix;

    The calculating unit is further configured to separately perform feature decomposition on each of the distance matrices to obtain a corresponding feature vector matrix;

    The calculating unit is further configured to calculate a matrix of entropy values corresponding to each of the feature vector matrices;

    The calculating unit is further configured to calculate an average value of all the entropy value matrices to obtain a difference matrix;

    The binarization unit is configured to perform binarization processing on the points in the difference matrix, and use the obtained binarized image as the feature image.
16. The channel identification device according to claim 15, wherein the calculation unit is further configured to generate a corresponding matrix according to each of the screen images in the process of extracting the image, and separately perform segmentation processing on each of the generated matrices. Obtaining a matrix corresponding to the identification image, and calculating the mean matrix based on the matrix of the identification image.
17. The channel identification device according to claim 14, wherein the identification extraction module comprises a cutting unit and an extraction unit;

    The cutting unit is configured to perform a sixteen-square grid cut on each of the screen images to obtain a corresponding area image group;

    The extracting unit is configured to extract an area image of the first row and the first column of each of the regional image groups as a corresponding identification image.
18. The channel identification device according to claim 14, wherein said channel identification device further comprises an initialization module;

    The initialization module is configured to load a preset library image and corresponding channel information to perform channel identification according to the library image and corresponding channel information.
19. The channel identifying apparatus according to claim 14, wherein said channel identifying means further comprises a statistic module;

    The statistic module is configured to perform statistics on channels locked by users in each identification period within a preset time period;

    The determining module is further configured to use a channel in which the number of locks in the statistical result is ranked as a preset channel as a preference channel of the user within the preset time.
20. The channel identification device according to claim 14, wherein the screen image is obtained by intercepting, and the channel identification device further comprises:

    a reading module for reading the resolution of the screen;

    a judging module, configured to determine whether the read screen resolution meets a preset reference sample specification;

    The collecting module is further configured to: if the screen resolution meets the reference sample specification, Collecting a plurality of screen images in a preset recognition period, and if the screen resolution does not conform to the reference sample specification, performing interpolation processing each time the screen image is captured, and using the screen image after the interpolation processing as the collection To the image.

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