CN111275096A - Homonymous cell identification method and system based on image identification - Google Patents
Homonymous cell identification method and system based on image identification Download PDFInfo
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Abstract
The invention discloses a method and a system for identifying a cell with the same name based on image identification, wherein the method comprises the following steps: acquiring first/second attribute information of a first/second cell to be distinguished; determining the distance between the two cells according to the first longitude and latitude information and the second longitude and latitude information; when the distance is smaller than or equal to a preset threshold value, acquiring a first image/a second image of a first cell/a second cell to be distinguished; carrying out significance detection on the first image and the second image, and obtaining a first/second area where the first/second building is located according to a significance detection result; calculating an image similarity value between the first region and the second region; and determining a discrimination result according to the image similarity value and the distance between the two cells. After the first stair and the second building are identified by the saliency detection algorithm, the similarity of the first stair and the second building can be measured in a balanced manner by calculating the image similarity value between the first building and the second building, so that whether the same-name cells are the same cell or not is judged, and the judgment accuracy is effectively improved.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a method and a system for identifying a homonymous cell based on image recognition.
Background
With the rapid popularization and development of the internet, a house renting and selling platform is greatly popularized. And the house broker issues the house source information to each renting and selling platform so that the user can conveniently search the required house source information on the house source website by setting a screening condition.
However, in some application scenarios, if the alias of the cell a is the cell B, different house brokers may use different cell names when issuing the house source information, which results in that a user cannot distinguish whether the two are the same house source when searching the house source information; in addition, in another application scenario, if there are two cells with the same or similar names, the user may misunderstand that the two cells are the same house source.
In order to solve the above problem, the method for determining whether two cell names are the same cell in the prior art is as follows: firstly, judging whether the cities and the urban areas where the two cells are located are the same; and if the two cell names are the same, further calculating the text similarity of the two cell names, and if the text similarity is more than or equal to 90%, judging that the two cells are the same cell.
However, in the above method for identifying the cells with the same name, when an alias with text similarity smaller than 90% exists in a certain cell, or text similarity of names of two different cells exceeds 90%, a high misjudgment frequency occurs, and the identification accuracy is greatly reduced.
Disclosure of Invention
The invention provides a method and a system for identifying a homonymous cell based on image identification, which can effectively improve the identification accuracy of the homonymous cell and reduce the misjudgment risk.
In a first aspect, the present application provides a method for identifying a cell of the same name based on image recognition, the method comprising:
acquiring first attribute information of a first cell to be identified and second attribute information of a second cell to be identified; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified;
determining the distance between the first cell to be distinguished and the second cell to be distinguished according to the first longitude and latitude information and the second longitude and latitude information;
when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value, acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified;
performing significance detection on the first image and the second image, and obtaining a first area where the first building is located and a second area where the second building is located according to a significance detection result;
calculating an image similarity value between the first region and the second region;
and determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated.
Optionally, the step of performing saliency detection on the first image and the second image, and obtaining a first region where the first building is located and a second region where the second building is located according to a saliency detection result includes:
calculating a first saliency map corresponding to the first image and a second saliency map corresponding to the second image;
according to a preset significance threshold value, a minimum circumscribed rectangle where a first building is located is segmented from the first significance map to serve as a first area; and dividing the minimum circumscribed rectangle where the second building is located from the second saliency map to serve as a second region.
Optionally, the step of calculating a first saliency map corresponding to the first image and a second saliency map corresponding to the second image includes:
performing fast Fourier transform on the first image and the second image to obtain a first magnitude spectrum and a first phase spectrum of the first image, and a second magnitude spectrum and a second phase spectrum of the second image;
calculating a first logarithmic magnitude spectrum of the first image according to the first magnitude spectrum, and calculating a second logarithmic magnitude spectrum of the second image according to the second magnitude spectrum;
respectively filtering the first logarithmic magnitude spectrum and the second logarithmic magnitude spectrum by using a preset mean filter, and calculating a first residual spectrum of the first image and a second residual spectrum of the second image according to the following formulas:
R1＝L1-hn*L1；
R2＝L2-hn*L2；
in the formula, R1For the calculated first residual spectrum, R2For the calculated second residual spectrum, L1Is a first log-amplitude spectrum of the first image, L2Is the second log-amplitude spectrum of the second image, hnA mean filter representing a preset size of nxn;
and reconstructing by using the first residual spectrum and the first phase spectrum to obtain a first saliency map corresponding to a first image, and reconstructing by using the second residual spectrum and the second phase spectrum to obtain a second saliency map corresponding to a second image.
Optionally, the image similarity value between the first region and the second region is calculated by using the following formula:
SIM(L1，L2)＝I(L1，L2)·C(L1，L2)·S(L1，L2)；
in the formula, L1Represents the first region, L2Denotes the second region, I (L)1，L2) Representing the similarity of luminance between the first and second regions, C (L)1，L2) Representing the similarity of contrast between the first and second regions, S (L)1，L2) Representing structural similarity between the first region and the second region; wherein the content of the first and second substances,
in the formula u1、u2Respectively representing the mean value of all pixel values in the first area and the mean value of all pixel values in the second area; sigma1、σ2Respectively representing the standard deviation of all pixel values in the first area and the standard deviation of all pixel values in the second area; sigma12Representing a covariance of the first region and the second region; c1＝(k1·L)2、C2＝(k2·L)2、C3＝C2/2，k1＝0.01，k2＝0.03，L＝255。
Optionally, the first attribute information further includes a first city and a first urban area where the first cell to be identified is located, and the second attribute information further includes a second city and a second urban area where the second cell to be identified is located;
before the step of determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information, the method further includes:
comparing whether the first city in which the first cell to be distinguished is located is the same as the second city in which the second cell to be distinguished is located; if the cell number is different from the first cell number, the discrimination result is that the first cell to be discriminated and the second cell to be discriminated are not the same cell;
if so, comparing whether a first urban area in which the first cell to be distinguished is located is the same as a second urban area in which the second cell to be distinguished is located: if the cell number is different from the first cell number, the discrimination result is that the first cell to be discriminated and the second cell to be discriminated are not the same cell; and if the first longitude and latitude information and the second longitude and latitude information are the same, determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information.
Optionally, the first longitude and latitude information includes a first longitude and a first latitude of a first cell to be distinguished, and the second longitude and latitude information includes a second longitude and a second latitude of a second cell to be distinguished;
the distance between the first cell to be distinguished and the second cell to be distinguished is obtained by adopting the following formula:
haversin(θ)＝sin2(θ/2)＝(1-cos(θ))/2；
wherein d is the distance between the first cell to be discriminated and the second cell to be discriminated, R is the radius of the earth,
and
first and second latitudes, respectively, and Δ λ represents the difference between the first and second longitudes.
Optionally, the step of determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated includes:
when the image similarity value is greater than or equal to 0.9, the first cell to be distinguished and the second cell to be distinguished are the same cell according to the distinguishing result;
when the image similarity value is greater than or equal to 0.6, judging whether the distance between the first cell to be distinguished and the second cell to be distinguished is less than or equal to 300 meters; if so, determining that the first cell to be distinguished and the second cell to be distinguished are the same cell; if not, the first cell to be distinguished and the second cell to be distinguished are not the same cell;
and when the distance between the first cell to be distinguished and the second cell to be distinguished is less than or equal to 10 meters, the distinguishing result is that the first cell to be distinguished and the second cell to be distinguished are the same cell.
In a second aspect, the present application provides a system for identifying a cell of the same name based on image recognition, the system comprising:
the information acquisition module is used for acquiring first attribute information of a first cell to be identified and second attribute information of a second cell to be identified; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified;
the distance determining module is used for determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information;
the image acquisition module is used for acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified;
the saliency detection module is used for carrying out saliency detection on the first image and the second image and obtaining a first area where the first building is located and a second area where the second building is located according to a saliency detection result;
a calculation module for calculating an image similarity value between the first region and the second region;
and the result determining module is used for determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated.
In a third aspect, the present application provides an electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method as described in any one of the first aspects above.
In a fourth aspect, the present application provides a computer readable medium having stored thereon a computer program which, when executed by a processor, performs the method as described in any of the first aspects above.
Compared with the prior art, the homonymous cell identification method and system based on image identification provided by the invention at least realize the following beneficial effects:
according to the method and the system for identifying the homonymous cell based on the image identification, first attribute information of a first cell to be identified and second attribute information of a second cell to be identified are obtained; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified; determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information; when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value, acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified; performing significance detection on the first image and the second image, and obtaining a first area where the first building is located and a second area where the second building is located according to a significance detection result; calculating an image similarity value between the first region and the second region; and determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated. Because the outward appearance of each building body is the same or similar in same district, this application utilizes the saliency detection algorithm discernment first stair and second building body after, through calculating the image similarity value between first building body and the second building body, can measure out the similarity degree of the two by balance to whether the district of the same name is same district, effectively improved the discernment rate of accuracy.
Of course, it is not necessary for any product in which the present invention is practiced to achieve all of the above-described technical effects simultaneously.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart illustrating a method for identifying a cell of the same name based on image recognition according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a system for identifying a cell of the same name based on image recognition according to an embodiment of the present application;
FIG. 3 illustrates an exemplary system architecture diagram that may be used with embodiments of the present application;
fig. 4 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present application.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In the method for distinguishing the cells with the same name provided by the prior art, for the cells to be distinguished in the same city and urban area, whether the cells are the same cell is judged only by judging whether the text similarity is greater than or equal to a preset threshold value. It can be understood that if a name a and an alias B exist in a certain cell, and the text similarity between a and B is less than 90%, the name a and the alias B are determined to be different cells; for another example, because the names of two cells with names a and B are the same or similar, the text similarity is greater than or equal to 90%, and then the name a and the alias B are determined to be the same cell. Therefore, the method for distinguishing the cells with the same name provided by the prior art only takes the text similarity as a distinguishing basis, so that high misjudgment frequency can occur, and the distinguishing accuracy is greatly reduced.
In view of the above, the invention provides a method and a system for identifying a cell with the same name based on image recognition, which can effectively improve the identification accuracy of the cell with the same name and reduce the risk of misjudgment.
The following detailed description is to be read in connection with the drawings and the detailed description.
Fig. 1 is a flowchart illustrating a method for identifying a cell of the same name based on image recognition according to an embodiment of the present application. Referring to fig. 1, the method for identifying a cell of the same name includes:
103, when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value, acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified;
104, performing significance detection on the first image and the second image, and obtaining a first area where the first building is located and a second area where the second building is located according to a significance detection result;
and step 106, determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated.
Specifically, after first longitude and latitude information of a first cell to be identified and second longitude and latitude information of a second cell to be identified are obtained, a distance between the two cells can be calculated, and preliminary identification is performed in a distance limiting mode; then, for two cells to be distinguished which may be the same cell, identifying and obtaining a first region where a first building is located and a second region where a second building is located by using a significance detection algorithm; generally, the appearances of buildings in the same cell, such as the structure of the buildings and the color of each facade in the buildings, are the same or similar, so that the image similarity value between the first area and the second area can be further calculated; obviously, the larger the image similarity value is, the more similar the first building in the first area and the second building in the second area are; conversely, the smaller the image similarity value, the smaller the degree of similarity between the first building in the first area and the second building in the second area. And finally, combining the image similarity value and the distance between the two cells to determine a discrimination result.
In the method for identifying the cells with the same name provided by the embodiment of the application, the similarity degree of the first building and the second building is measured by calculating the image similarity value between the first building and the second building, and compared with the identification method only considering the text similarity in the prior art, the method can be used for identifying by combining the building characteristics in the first cell to be identified and the second cell to be identified, so that the misjudgment risk can be reduced, and the identification accuracy is effectively improved.
Optionally, in the step 104, the step of performing saliency detection on the first image and the second image, and obtaining a first region where the first building is located and a second region where the second building is located according to a saliency detection result includes:
calculating a first saliency map corresponding to the first image and a second saliency map corresponding to the second image;
according to a preset significance threshold value, a minimum circumscribed rectangle where a first building is located is segmented from a first significance map and serves as a first area; and the minimum circumscribed rectangle where the second building is located is divided from the second saliency map to be used as a second area.
Specifically, the pixel value at each pixel point in the saliency map represents the saliency at the corresponding position of the original image; the larger the pixel value is, the higher the degree of saliency at that position is represented, and the larger the luminance in the saliency map is; the smaller the pixel value, the lower the saliency at that position, and the smaller the luminance in the saliency map.
Illustratively, since the pixel values of the respective pixel points in the first saliency map and the second saliency map are within [0, 255], the preset saliency threshold may be 150. And according to the significance threshold value, performing binarization processing on the first significance map and the second significance map so as to segment the building body from the background.
It will be appreciated that saliency detection models the visual characteristics of the human eye, and is able to calculate the regions of the current scene that are most interesting to the user and most representative of the image content, and the saliency map is a reflection of these regions. The building body is identified by the significance detection algorithm, the identification result with higher consistency with the human eye observation result can be obtained, and the first area and the second area can be more accurately segmented, so that the calculated image similarity value is more accurate.
Optionally, in this embodiment, a residual spectrum algorithm may be used to calculate a first saliency map corresponding to the first image and a second saliency map corresponding to the second image, and specifically includes:
s1, performing fast Fourier transform on the first image and the second image to obtain a first magnitude spectrum and a first phase spectrum of the first image and a second magnitude spectrum and a second phase spectrum of the second image;
s2, calculating a first logarithmic magnitude spectrum of the first image according to the first magnitude spectrum, and calculating a second logarithmic magnitude spectrum of the second image according to the second magnitude spectrum;
s3, filtering the first logarithmic magnitude spectrum and the second logarithmic magnitude spectrum respectively by using a preset mean filter, and calculating a first residual spectrum of the first image and a second residual spectrum of the second image according to the following formulas:
R1＝L1-hn*L1；
R2＝L2-hn*L2；
in the formula, R1For the calculated first residual spectrum, R2For the calculated second residual spectrum, L1Is a first log-amplitude spectrum of the first image, L2Is the second log-amplitude spectrum of the second image, hnA mean filter representing a preset size of nxn;
and S4, reconstructing by using the first residual spectrum and the first phase spectrum to obtain a first saliency map corresponding to the first image, and reconstructing by using the second residual spectrum and the second phase spectrum to obtain a second saliency map corresponding to the second image.
In step S1, if the first image and the second image are color images, the first image and the second image may be converted into grayscale images, and then fast fourier transform is performed on each pixel point in the converted grayscale images.
Specifically, the first logarithmic magnitude spectrum of the first image and the second logarithmic magnitude spectrum of the second image may be calculated according to the following formulas:
L1＝log(A1)；
L2＝log(A2)；
wherein A is1、A2Respectively representing a first magnitude spectrum of the first image and a second magnitude spectrum of the second image, L1、L2A first log-magnitude spectrum of the first image and a second log-magnitude spectrum of the second image are represented, respectively.
In step S3, a preset average filter hnThe size of (d) may be 3 × 3; the first residual spectrum R is obtained by calculation1And a second residual spectrum R2Representing the novel information contained in the first image and the second image, respectively.
In step S4, the first saliency map and the second saliency map may be reconstructed according to the following formula:
S1＝g*f-1[exp(R1+P1)]2；
S2＝g*f-1[exp(R2+P2)]2；
wherein, P1、P2Representing a first phase spectrum of the first image and a second phase spectrum of the second image, R, respectively1、R2A first residual spectrum of the first image and a second residual spectrum of the second image, f-1For inverse Fourier transform, g is a Gaussian filter, S1、S2The first significance map and the second significance map are obtained through calculation respectively.
Optionally, the image similarity value between the first region and the second region is calculated by using the following formula:
SIM(L1，L2)＝I(L1，L2)·C(L1，L2)·S(L1，L2)；
in the formula, L1Denotes a first region, L2Denotes a second region, I (L)1，L2) Representing the similarity of luminance between the first and second regions, C (L)1，L2) Representing the similarity of contrast between the first and second regions, S (L)1，L2) Representing structural similarity between the first region and the second region; wherein the content of the first and second substances,
in the formula u1、u2Respectively representing the mean value of all pixel values in the first area and the mean value of all pixel values in the second area; sigma1、σ2Respectively representing the standard deviation of all pixel values in the first area and the standard deviation of all pixel values in the second area; sigma12Representing a covariance of the first region and the second region; c1＝(k1·L)2、C2＝(k2·L)2、C3＝C2/2，k1＝0.01，k2＝0.03，L＝255。
Further, in this embodiment, before the image similarity value is calculated, the first region and the second region may be normalized to the same size, so as to avoid the problem that the image similarity value is not calculated accurately due to different scales.
Because the brightness similarity, the contrast similarity and the structure similarity between the first region and the second region are respectively calculated, the similarity between the two regions can be measured from multiple angles, and the accuracy and the reliability of the identification result of the cells with the same name are ensured.
Optionally, the first attribute information further includes a first city and a first urban area where the first cell to be identified is located, and the second attribute information further includes a second city and a second urban area where the second cell to be identified is located;
before the step 102, it may also be determined whether the first attribute information and the second attribute information are the same, specifically including:
comparing whether a first city in which the first cell to be distinguished is located is the same as a second city in which the second cell to be distinguished is located; if the cell number is different from the first cell number, the discrimination result is that the first cell to be discriminated and the second cell to be discriminated are not the same cell;
if so, comparing whether a first urban area in which the first cell to be distinguished is located is the same as a second urban area in which the second cell to be distinguished is located: if the cell number is different from the first cell number, the discrimination result is that the first cell to be discriminated and the second cell to be discriminated are not the same cell; and if so, executing the step of determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information.
It will be appreciated that if the two cells to be distinguished are the same cell, then both must also be in the same city and downtown area, i.e.: two cells to be distinguished in different cities or urban areas must not be the same cell. Therefore, aiming at the condition that the cell to be distinguished is in different cities or urban areas, the distinguishing result can be quickly determined by comparing the first basic information with the second basic information without subsequent calculation, so that the calculation resources are greatly saved, and the real-time performance of the algorithm is improved.
Optionally, the first longitude and latitude information includes a first longitude and a first latitude of the first cell to be distinguished, and the second longitude and latitude information includes a second longitude and a second latitude of the second cell to be distinguished;
the distance between the first cell to be distinguished and the second cell to be distinguished is obtained by adopting the following formula:
haversin(θ)＝sin2(θ/2)＝(1-cos(θ))/2；
wherein d is the distance between the first cell to be discriminated and the second cell to be discriminated, R is the radius of the earth,
and
first and second latitudes, respectively, and Δ λ represents the difference between the first and second longitudes.
In this embodiment, the first longitude and latitude information and the second longitude and latitude information may be obtained by a map App, and the radius of the earth may be 6371 km. The earth is a sphere, and the longitude and latitude can be converted into the earth coordinate by using the formula, so that the distance between the cells to be distinguished can be accurately calculated.
Optionally, the step of determining the discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated includes:
when the image similarity value is greater than or equal to 0.9, the discrimination result is that the first cell to be discriminated and the second cell to be discriminated are the same cell;
when the image similarity value is greater than or equal to 0.6, judging whether the distance between the first cell to be distinguished and the second cell to be distinguished is less than or equal to 300 meters; if so, the discrimination result is that the first cell to be discriminated and the second cell to be discriminated are the same cell; if not, the first cell to be distinguished and the second cell to be distinguished are not the same cell;
and when the distance between the first cell to be distinguished and the second cell to be distinguished is less than or equal to 10 meters, the distinguishing result is that the first cell to be distinguished and the second cell to be distinguished are the same cell.
Whether the same-name cells are the same cell or not is distinguished, so that whether the same house resources exist on the house renting and selling platform or not can be further checked when the house brokerage distributes house resource information, subsequent house resource combination or house resource duplication elimination is facilitated, a user can quickly and accurately acquire required information in massive house resource data, and the use experience of the user is improved.
The homonymous cell distinguishing method based on image recognition provided by the invention at least realizes the following beneficial effects:
acquiring first attribute information of a first cell to be identified and second attribute information of a second cell to be identified; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified; determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information; when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value, acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified; performing significance detection on the first image and the second image, and obtaining a first area where the first building is located and a second area where the second building is located according to a significance detection result; calculating an image similarity value between the first region and the second region; and determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated. Because the outward appearance of each building body is the same or similar in same district, this application utilizes the saliency detection algorithm discernment first stair and second building body after, through calculating the image similarity value between first building body and the second building body, can measure out the similarity degree of the two by balance to whether the district of the same name is same district, effectively improved the discernment rate of accuracy.
Based on the same inventive concept, the present application further provides a system for identifying a cell of the same name based on image recognition, and fig. 2 is a schematic structural diagram of the system for identifying a cell of the same name based on image recognition provided in the embodiment of the present application. Referring to fig. 2, the system includes:
an information obtaining module 210, configured to obtain first attribute information of a first cell to be identified and second attribute information of a second cell to be identified; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified;
a distance determining module 220, configured to determine a distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information;
an image obtaining module 230, configured to obtain a first image of the first cell to be identified and a second image of the second cell to be identified when a distance between the first cell to be identified and the second cell to be identified is less than or equal to a preset threshold; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified;
the saliency detection module 240 is configured to perform saliency detection on the first image and the second image, and obtain a first area where the first building is located and a second area where the second building is located according to a saliency detection result;
a calculating module 250 for calculating an image similarity value between the first region and the second region;
a result determining module 260, configured to determine a discrimination result according to the image similarity value and a distance between the first cell to be discriminated and the second cell to be discriminated.
Because the appearances of all buildings in the same cell are the same or similar, the method for distinguishing the same-name cell provided by the application can measure the similarity degree of the first stair and the second stair by calculating the image similarity value between the first building and the second building after the first stair and the second stair are identified by utilizing the significance detection algorithm, thereby distinguishing whether the same-name cell is the same cell or not, and effectively improving the distinguishing accuracy.
The homonymous cell identification method and system based on image identification provided by the invention at least realize the following beneficial effects:
acquiring first attribute information of a first cell to be identified and second attribute information of a second cell to be identified; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified; determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information; when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value, acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified; performing significance detection on the first image and the second image, and obtaining a first area where the first building is located and a second area where the second building is located according to a significance detection result; calculating an image similarity value between the first region and the second region; and determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated. Because the outward appearance of each building body is the same or similar in same district, this application utilizes the saliency detection algorithm discernment first stair and second building body after, through calculating the image similarity value between first building body and the second building body, can measure out the similarity degree of the two by balance to whether the district of the same name is same district, effectively improved the discernment rate of accuracy.
Fig. 3 is a diagram illustrating an exemplary system architecture to which the method or system for identifying a cell of the same name based on image recognition according to the embodiment of the present invention can be applied.
As shown in fig. 3, the system architecture 300 may include terminal devices 301, 302, 303, a network 304, and a server 305. The network 304 serves as a medium for providing communication links between the terminal devices 301, 302, 303 and the server 305. Network 304 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal device 301, 302, 303 to interact with the server 305 via the network 304 to receive or send messages or the like. The terminal devices 301, 302, 303 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 301, 302, 303 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 305 may be a server providing various services, such as a background management server (for example only) providing support for shopping-like websites browsed by users using the terminal devices 301, 302, 303. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the method for identifying a cell with the same name based on image recognition provided by the embodiment of the present invention is generally executed by the server 305, and accordingly, a system for identifying a cell with the same name based on image recognition is generally disposed in the server 305.
It should be understood that the number of terminal devices, networks, and servers in fig. 3 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 4, a block diagram of a computer system 400 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the system 400 are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program performs the above-described functions defined in the system of the present invention when executed by a Central Processing Unit (CPU) 401.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, 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 the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an information acquisition module, a distance determination module, an image acquisition module, a saliency detection module, a calculation module, and a result determination module. Wherein the names of the modules do not in some cases constitute a limitation of the module itself.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:
acquiring first attribute information of a first cell to be identified and second attribute information of a second cell to be identified; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified;
determining the distance between the first cell to be distinguished and the second cell to be distinguished according to the first longitude and latitude information and the second longitude and latitude information;
when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value, acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified;
performing significance detection on the first image and the second image, and obtaining a first area where the first building is located and a second area where the second building is located according to a significance detection result;
calculating an image similarity value between the first region and the second region;
and determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated.
Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustrative purposes only and are not intended to limit the scope of the present invention. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (10)
1. A method for identifying a cell with the same name based on image recognition, the method comprising:
acquiring first attribute information of a first cell to be identified and second attribute information of a second cell to be identified; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified;
determining the distance between the first cell to be distinguished and the second cell to be distinguished according to the first longitude and latitude information and the second longitude and latitude information;
when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value, acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified;
performing significance detection on the first image and the second image, and obtaining a first area where the first building is located and a second area where the second building is located according to a significance detection result;
calculating an image similarity value between the first region and the second region;
and determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated.
2. The method for identifying the cells with the same name based on the image recognition as claimed in claim 1, wherein the step of performing the significance detection on the first image and the second image and obtaining a first region where the first building is located and a second region where the second building is located according to the significance detection result comprises:
calculating a first saliency map corresponding to the first image and a second saliency map corresponding to the second image;
according to a preset significance threshold value, a minimum circumscribed rectangle where a first building is located is segmented from the first significance map to serve as a first area; and dividing the minimum circumscribed rectangle where the second building is located from the second saliency map to serve as a second region.
3. The method for identifying homonymous cells based on image recognition according to claim 2, wherein the step of calculating a first saliency map corresponding to the first image and a second saliency map corresponding to the second image comprises:
performing fast Fourier transform on the first image and the second image to obtain a first magnitude spectrum and a first phase spectrum of the first image, and a second magnitude spectrum and a second phase spectrum of the second image;
calculating a first logarithmic magnitude spectrum of the first image according to the first magnitude spectrum, and calculating a second logarithmic magnitude spectrum of the second image according to the second magnitude spectrum;
respectively filtering the first logarithmic magnitude spectrum and the second logarithmic magnitude spectrum by using a preset mean filter, and calculating a first residual spectrum of the first image and a second residual spectrum of the second image according to the following formulas:
R1＝L1-hn*L1；
R2＝L2-hn*L2；
in the formula, R1For the calculated first residual spectrum, R2For the calculated second residual spectrum, L1Is a first log-amplitude spectrum of the first image, L2Is a second logarithmic magnitude of a second imageSpectrum, hnA mean filter representing a preset size of nxn;
and reconstructing by using the first residual spectrum and the first phase spectrum to obtain a first saliency map corresponding to a first image, and reconstructing by using the second residual spectrum and the second phase spectrum to obtain a second saliency map corresponding to a second image.
4. The method for identifying the cells of the same name based on the image recognition according to claim 3, wherein the image similarity value between the first region and the second region is calculated by the following formula:
SIM(L1，L2)＝I(L1，L2)·C(L1，L2)·S(L1，L2)；
in the formula, L1Represents the first region, L2Denotes the second region, I (L)1，L2) Representing the similarity of luminance between the first and second regions, C (L)1，L2) Representing the similarity of contrast between the first and second regions, S (L)1，22) Representing structural similarity between the first region and the second region; wherein the content of the first and second substances,
in the formula u1、u2Respectively representing the mean value of all pixel values in the first area and the mean value of all pixel values in the second area; sigma1、σ2Respectively representing the standard deviation of all pixel values in the first area and the standard deviation of all pixel values in the second area; sigma12Representing a covariance of the first region and the second region; c1＝(k1·L)2、C2＝(k2·L)2、C3＝C2/2，k1＝0.01，k2＝0.03，L＝255。
5. The method for identifying homonymous cells based on image recognition according to claim 1, wherein the first attribute information further includes a first city and a first urban area where the first cell to be identified is located, and the second attribute information further includes a second city and a second urban area where the second cell to be identified is located;
before the step of determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information, the method further includes:
comparing whether a first city in which the first cell to be distinguished is located is the same as a second city in which the second cell to be distinguished is located; if the cell number is different from the first cell number, the discrimination result is that the first cell to be discriminated and the second cell to be discriminated are not the same cell;
if so, comparing whether a first urban area in which the first cell to be distinguished is located is the same as a second urban area in which the second cell to be distinguished is located: if the cell number is different from the first cell number, the discrimination result is that the first cell to be discriminated and the second cell to be discriminated are not the same cell; and if the first longitude and latitude information and the second longitude and latitude information are the same, determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information.
6. The method for identifying the cells of the same name based on the image recognition according to claim 5, wherein the first longitude and latitude information comprises a first longitude and a first latitude of a first cell to be identified, and the second longitude and latitude information comprises a second longitude and a second latitude of a second cell to be identified;
the distance between the first cell to be distinguished and the second cell to be distinguished is obtained by adopting the following formula:
haversin(θ)＝sin2(θ/2)＝(1-cos(θ))/2；
wherein d is the distance between the first cell to be discriminated and the second cell to be discriminated, R is the radius of the earth,
and
first and second latitudes, respectively, and Δ λ represents the difference between the first and second longitudes.
7. The method for cell discrimination of the same name based on image recognition according to claim 1, wherein the step of determining the discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated comprises:
when the image similarity value is greater than or equal to 0.9, the first cell to be distinguished and the second cell to be distinguished are the same cell according to the distinguishing result;
when the image similarity value is greater than or equal to 0.6, judging whether the distance between the first cell to be distinguished and the second cell to be distinguished is less than or equal to 300 meters; if so, determining that the first cell to be distinguished and the second cell to be distinguished are the same cell; if not, the first cell to be distinguished and the second cell to be distinguished are not the same cell;
and when the distance between the first cell to be distinguished and the second cell to be distinguished is less than or equal to 10 meters, the distinguishing result is that the first cell to be distinguished and the second cell to be distinguished are the same cell.
8. A system for identifying a cell of a same name based on image recognition, the system comprising:
the information acquisition module is used for acquiring first attribute information of a first cell to be identified and second attribute information of a second cell to be identified; the first attribute information comprises first longitude and latitude information of a first cell to be identified, and the second attribute information comprises second longitude and latitude information of a second cell to be identified;
the distance determining module is used for determining the distance between the first cell to be identified and the second cell to be identified according to the first longitude and latitude information and the second longitude and latitude information;
the image acquisition module is used for acquiring a first image of the first cell to be distinguished and a second image of the second cell to be distinguished when the distance between the first cell to be distinguished and the second cell to be distinguished is smaller than or equal to a preset threshold value; the first image comprises a first building body of a first cell to be identified, and the second image comprises a second building body of a second cell to be identified;
the saliency detection module is used for carrying out saliency detection on the first image and the second image and obtaining a first area where the first building is located and a second area where the second building is located according to a saliency detection result;
a calculation module for calculating an image similarity value between the first region and the second region;
and the result determining module is used for determining a discrimination result according to the image similarity value and the distance between the first cell to be discriminated and the second cell to be discriminated.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107067030A (en) * | 2017-03-29 | 2017-08-18 | 北京小米移动软件有限公司 | The method and apparatus of similar pictures detection |
| CN107346409A (en) * | 2016-05-05 | 2017-11-14 | 华为技术有限公司 | Pedestrian recognition methods and device again |
| CN108399192A (en) * | 2018-01-25 | 2018-08-14 | 链家网（北京）科技有限公司 | A kind of cell information matching process and device |
| CN108537239A (en) * | 2018-04-18 | 2018-09-14 | 西南科技大学 | A kind of method of saliency target detection |
| CN109447154A (en) * | 2018-10-29 | 2019-03-08 | 网易（杭州）网络有限公司 | Picture similarity detection method, device, medium and electronic equipment |
| CN109948644A (en) * | 2019-01-21 | 2019-06-28 | 深圳壹账通智能科技有限公司 | A kind of similar source of houses data detection method, device and terminal device |
| CN109977287A (en) * | 2019-03-28 | 2019-07-05 | 国家计算机网络与信息安全管理中心 | A kind of house property data identity method of discrimination of different aforementioned sources |
| CN110400024A (en) * | 2019-07-31 | 2019-11-01 | 京东城市(北京)数字科技有限公司 | Method, apparatus, equipment and the computer readable storage medium of order forecasting |
| CN110490264A (en) * | 2019-08-23 | 2019-11-22 | 中国民航大学 | Multidimensional distance cluster method for detecting abnormality and system based on time series |
| CN110633383A (en) * | 2019-09-12 | 2019-12-31 | 北京无限光场科技有限公司 | Method and device for identifying repeated house sources, electronic equipment and readable medium |
-
2020
- 2020-01-17 CN CN202010054513.6A patent/CN111275096A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107346409A (en) * | 2016-05-05 | 2017-11-14 | 华为技术有限公司 | Pedestrian recognition methods and device again |
| CN107067030A (en) * | 2017-03-29 | 2017-08-18 | 北京小米移动软件有限公司 | The method and apparatus of similar pictures detection |
| CN108399192A (en) * | 2018-01-25 | 2018-08-14 | 链家网（北京）科技有限公司 | A kind of cell information matching process and device |
| CN108537239A (en) * | 2018-04-18 | 2018-09-14 | 西南科技大学 | A kind of method of saliency target detection |
| CN109447154A (en) * | 2018-10-29 | 2019-03-08 | 网易（杭州）网络有限公司 | Picture similarity detection method, device, medium and electronic equipment |
| CN109948644A (en) * | 2019-01-21 | 2019-06-28 | 深圳壹账通智能科技有限公司 | A kind of similar source of houses data detection method, device and terminal device |
| CN109977287A (en) * | 2019-03-28 | 2019-07-05 | 国家计算机网络与信息安全管理中心 | A kind of house property data identity method of discrimination of different aforementioned sources |
| CN110400024A (en) * | 2019-07-31 | 2019-11-01 | 京东城市(北京)数字科技有限公司 | Method, apparatus, equipment and the computer readable storage medium of order forecasting |
| CN110490264A (en) * | 2019-08-23 | 2019-11-22 | 中国民航大学 | Multidimensional distance cluster method for detecting abnormality and system based on time series |
| CN110633383A (en) * | 2019-09-12 | 2019-12-31 | 北京无限光场科技有限公司 | Method and device for identifying repeated house sources, electronic equipment and readable medium |
Non-Patent Citations (1)
| Title |
|---|
| UNIQUE-YOU: "显著性检测方法SR（谱残余方法）", 《CSDN》 * |
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