CN110909766A - Similarity determination method and device, storage medium and electronic device - Google Patents
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Abstract
The invention provides a method and a device for determining similarity, wherein the method comprises the following steps: obtaining M first objects and M second objects in the pictures, wherein the M first objects and the second objects are placed in the pictures at preset positions, N is an integer larger than 1, and M is an integer larger than 0; acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions; and determining an M-dimensional feature vector of each picture according to the M position coordinates, and determining the similarity of every two pictures in the N pictures according to the M-dimensional feature vector of each picture.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method and an apparatus for determining similarity, a storage medium, and an electronic apparatus.
Background
The image similarity comparison algorithm is very widely applied and can be applied to the fields of image searching, image de-duplication and the like. It is common practice to set two pictures within a certain distance to be similar by extracting features at the pixel level or extracting features through a neural network and then calculating the distance. However, in a certain field, the shape of some objects (such as dumplings) may change with time, and the characteristics are not obvious, and when two pictures are judged to be dumplings in the same dish, the accuracy of the former method is low.
Aiming at the problems of low accuracy rate and the like in the judgment process of the similarity of some objects in the related technology, no effective solution exists at present.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining similarity and a storage medium, which are used for solving the problems of low accuracy and the like in the process of judging the similarity of some objects in the related art.
According to an embodiment of the present invention, there is provided a method for determining similarity, including: for each of N pictures, identifying M first objects and second objects in the picture, wherein the M first objects and the second objects are placed in a predetermined position in the picture, N is an integer greater than 1, and M is an integer greater than 0; acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions; and determining an M-dimensional feature vector of each picture according to the M position coordinates, and determining the similarity of every two pictures in the N pictures according to the M-dimensional feature vector of each picture.
In an embodiment of the present invention, before identifying M first objects and M second objects in a picture for each of N pictures, the method further includes: and shooting the M first objects and the second objects for N times at the same preset angle to obtain N pictures.
In an embodiment of the present invention, acquiring M position coordinates of the first center positions of the M first objects with respect to the second center positions includes: and sequentially taking M relative positions of the M first central positions relative to the second central position as the M position coordinates.
In the embodiment of the present invention, determining the similarity between every two pictures in N pictures according to the M-dimensional feature vector of each picture includes: and acquiring the distance of the feature vectors corresponding to any two pictures, and determining that the any two pictures are similar under the condition that the distance meets a preset condition.
According to another embodiment of the present invention, there is also provided a similarity determination apparatus including: the identification module is used for identifying M first objects and second objects in the pictures for each of N pictures, wherein the M first objects and the second objects are placed in the pictures at preset positions, N is an integer larger than 1, and M is an integer larger than 0; the acquisition module is used for acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions; and the determining module is used for determining the M-dimensional characteristic vector of each picture according to the M position coordinates and determining the similarity of every two pictures in the N pictures according to the M-dimensional characteristic vector of each picture.
In the embodiment of the present invention, the identification module is further configured to take N shots of the M first objects and the M second objects at the same preset angle, so as to obtain N pictures.
In an embodiment of the present invention, the obtaining module is configured to sequentially use M relative positions of the M first center positions with respect to the second center position as the M position coordinates.
In the embodiment of the present invention, the determining module is further configured to obtain a distance between feature vectors corresponding to any two pictures, and determine that any two pictures are similar when the distance satisfies a preset condition.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, for each picture in N pictures, M first objects and second objects in the picture are identified, wherein the M first objects and the second objects are placed in the picture at a predetermined position, N is an integer greater than 1, and M is an integer greater than 0; acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions; the technical scheme is adopted, the problems that in the related technology, the accuracy rate is low in the judgment process of the similarity of some objects and the like are solved, and the method for determining the similarity of the specified object with high accuracy is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a server of a similarity determination method according to an embodiment of the present invention;
FIG. 2 is a flow chart of an alternative method of similarity determination according to an embodiment of the present invention;
fig. 3 is a block diagram of an alternative similarity determination apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic view (one) of the placement of a first object and a second object according to an alternative embodiment of the present invention;
FIG. 5 is a schematic view (two) of the placement of a first object and a second object according to an alternative embodiment of the invention;
fig. 6 is a schematic view (iii) of the placement of the first object and the second object according to an alternative embodiment of the present invention;
fig. 7 is a schematic view (four) of the placement of the first object and the second object according to an alternative embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In the related art, for food adjacent to the shelf life, only a reminder is sent to inform that the food is about to have the shelf life, but the eating habits of the user are not considered and no recipe recommendation is given, so that even if the user knows that some food materials are close to the shelf life through the refrigerator reminder, the food materials close to the shelf life are difficult to process in the shelf life because the user does not know how to cook the dishes according to the food materials, and the existing refrigerator only plays a role in monitoring the shelf life simply.
The method provided by the embodiment of the invention can be executed in a computer terminal or similar equipment. Taking the example of the method running on a computer terminal as an example, fig. 1 is a hardware structure block diagram of a computer terminal of a method for determining similarity according to an embodiment of the present invention. As shown in fig. 1, the computer terminal 10 may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally, a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or with more functionality than that shown in FIG. 1.
The memory 104 may be used to store a computer program, for example, a software program and a module of application software, such as a computer program corresponding to a similarity determination method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
Fig. 2 is a flowchart of an alternative similarity determination method according to an embodiment of the present invention, which is applied to the computer terminal shown in fig. 1, and as shown in fig. 2, the flowchart includes the following steps:
step S202, for each picture in N pictures, identifying M first objects and M second objects in the picture, wherein the M first objects and the second objects are placed in the picture at a predetermined position, N is an integer greater than 1, and M is an integer greater than 0;
step S204, acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions;
step S206, determining an M-dimensional feature vector of each picture according to the M position coordinates, and determining the similarity of every two pictures in the N pictures according to the M-dimensional feature vector of each picture.
According to the invention, for each picture in N pictures, M first objects and second objects in the picture are identified, wherein the M first objects and the second objects are placed in the picture at a predetermined position, N is an integer greater than 1, and M is an integer greater than 0; acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions; the technical scheme is adopted, the problems that in the related technology, the accuracy rate is low in the judgment process of the similarity of some objects and the like are solved, and the method for determining the similarity of the specified object with high accuracy is improved.
In an embodiment of the present invention, before identifying M first objects and M second objects in a picture for each of N pictures, the method further includes: and shooting the M first objects and the second objects for N times at the same preset angle to obtain N pictures.
In an embodiment of the present invention, acquiring M position coordinates of the first center positions of the M first objects with respect to the second center positions includes: and sequentially taking M relative positions of the M first central positions relative to the second central position as the M position coordinates.
In the embodiment of the present invention, determining the similarity between every two pictures in N pictures according to the M-dimensional feature vector of each picture includes: and acquiring the distance of the feature vectors corresponding to any two pictures, and determining that the any two pictures are similar under the condition that the distance meets a preset condition.
In the embodiment of the present invention, when the distance satisfies a preset condition, determining that any two pictures are similar includes:
and under the condition that the distance determined by the cosine theorem mode and the characteristic vector meets the preset condition, determining that any two pictures are similar.
In the embodiment of the present invention, the condition that the preset condition is satisfied means that any two pictures are determined to be similar when the distance determined according to the feature vector is greater than the threshold.
In the embodiment of the present invention, if one of the two pictures compared with each other lacks part of content, a loop process may be set, and the content of the part of content object that is missing from the picture is randomly deleted from the other picture each time, so that the number of the remaining objects of the two pictures is the same, and the above processes are performed in a loop, and finally the similarity between the two pictures is determined.
Optionally, the first object is a dumpling, the second object is a plate for holding the dumpling, the characteristic of the dumpling is not obvious in an actual scene, and the characteristic of the dumpling changes as time passes through the plate for holding the dumpling. The experimental result verifies that the accuracy rate of the algorithm is high.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The present embodiment further provides a device for determining similarity, which is used to implement the foregoing embodiments and preferred embodiments, and the description that has been given is omitted for brevity. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a block diagram of an alternative similarity determination apparatus according to an embodiment of the present invention, and as shown in fig. 3, the apparatus includes:
an identifying module 30, configured to identify, for each of N pictures, M first objects and M second objects in the picture, where the M first objects and the second objects are located in predetermined positions in the picture, N is an integer greater than 1, and M is an integer greater than 0;
an obtaining module 32, configured to obtain first center positions of M first objects and second center positions of the second object, and obtain M position coordinates of the first center positions of the M first objects relative to the second center positions;
the determining module 34 is configured to determine an M-dimensional feature vector of each picture according to the M position coordinates, and determine similarity between every two pictures in the N pictures according to the M-dimensional feature vector of each picture.
According to the invention, for each picture in N pictures, M first objects and second objects in the picture are identified, wherein the M first objects and the second objects are placed in the picture at a predetermined position, N is an integer greater than 1, and M is an integer greater than 0; acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions; the technical scheme is adopted, the problems that in the related technology, the accuracy rate is low in the judgment process of the similarity of some objects and the like are solved, and the method for determining the similarity of the specified object with high accuracy is improved.
In this embodiment of the present invention, the identification module 30 is further configured to take N shots of the M first objects and the M second objects at the same preset angle, so as to obtain N pictures.
In this embodiment of the present invention, the obtaining module 32 is configured to sequentially use M relative positions of the M first center positions with respect to the second center position as the M position coordinates.
In this embodiment of the present invention, the determining module 34 is further configured to obtain a distance between feature vectors corresponding to any two pictures, and determine that any two pictures are similar when the distance satisfies a preset condition.
In the embodiment of the present invention, the determining module 34 is further configured to determine that any two pictures are similar when the distance determined by the cosine theorem and the feature vector satisfies a preset condition.
In the embodiment of the present invention, the condition that the preset condition is satisfied means that any two pictures are determined to be similar when the distance determined according to the feature vector is greater than the threshold.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
The following explains the determination process of the above similarity with an alternative embodiment, but is not intended to limit the technical solution of the embodiment of the present invention.
FIG. 4 is a schematic view (one) of the placement of a first object and a second object according to an alternative embodiment of the present invention; FIG. 5 is a schematic view (two) of the placement of a first object and a second object according to an alternative embodiment of the invention; fig. 6 is a schematic view (iii) of the placement of the first object and the second object according to an alternative embodiment of the present invention.
In an alternative embodiment of the present invention, the dumpling corresponds to the first object of the above embodiment, and the plate corresponds to the second object of the above embodiment.
Taking the pictures of figures 4-6 as examples, the same dish of dumplings is shown in figures 4 and 5, and the other dish of dumplings is shown in figure 6.
Step 1, dividing each dumpling and each plate by using a target dividing method.
And 2, positioning the center position of each dumpling and the center position of the plate after the dumplings are cut.
Step 3, regarding FIG. 1, the coordinates of the center position of the dish are
Take the example of the upper left dumpling and the coordinate of the center of the upper left dumpling is
A similar calculation method is also shown for fig. 5 and fig. 6.
Step 4, calculating the coordinate of the center position of the dumpling relative to the center position of the plate as
Wherein
Step 5 in FIG. 4, for 12 dumplings, construct vectors
As a feature vector to decide whether or not they are similar. The feature vector of FIG. 5 is
The feature vector of FIG. 6 is
And 6, calculating the distance of the characteristic vector by using the cosine of the intersection angle, wherein the distance in the graph 4 and the graph 5 is as follows:
the distances of fig. 5 and 6 are:
and 7, defining a threshold value of 0.9, and considering the similarity when the threshold value is greater than 0.9, otherwise, considering the dissimilarity. In the experiment, the similarity between fig. 4 and 5 was 0.99, and the similarity between fig. 5 and 6 was 0.58. The experimental result well verifies the algorithm.
And 8, as shown in the figure 7, some dumplings in the picture are partially shielded, so that the dumplings in a dish are not completely displayed, and at the moment, the feature extraction algorithm can be optimized through local features rather than global features. The design algorithm is as follows: if the similarity comparison is performed between fig. 7 and fig. 4, if fig. 7 shows only 11 dumplings with 1 dumpling being blocked, the initial loop is set for 12 times, each loop randomly deletes one dumpling from fig. 4, the coordinate vectors of the remaining 11 dumplings are compared with the similarity of fig. 7, if the coordinate vectors are larger than the set threshold, the dumplings are considered to be similar, and the loop is exited, otherwise, the loop is continued. If FIG. 7 shows 2 dumplings with occlusion and only 10 dumplings, then an initial cycle is set
And then, randomly deleting 2 dumplings from the graph 4 in each circulation, comparing the similarity of the coordinate vectors of the remaining 10 dumplings with the graph 4, judging the dumplings to be similar if the coordinate vectors are larger than a set threshold value, exiting the circulation, and otherwise continuing the circulation. By analogy, when the number of the blocked dumplings is 3 (the initial cycle number is set as
) And 4 (setting the initial cycle number to be
) The same decision logic. If more than 4 occluded dumplings are directly judged to be dissimilar.
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, for each of N pictures, identifying M first objects and M second objects in the picture, where the M first objects and the second objects are placed in the picture at predetermined positions, N is an integer greater than 1, and M is an integer greater than 0;
s2, acquiring first center positions of M first objects and second center positions of the second object, and acquiring M position coordinates of the first center positions of the M first objects relative to the second center positions;
and S3, determining the M-dimensional feature vector of each picture according to the M position coordinates, and determining the similarity of every two pictures in the N pictures according to the M-dimensional feature vector of each picture.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, for each of N pictures, identifying M first objects and M second objects in the picture, where the M first objects and the second objects are placed in the picture at predetermined positions, N is an integer greater than 1, and M is an integer greater than 0;
s2, acquiring first center positions of M first objects and second center positions of the second object, and acquiring M position coordinates of the first center positions of the M first objects relative to the second center positions;
and S3, determining the M-dimensional feature vector of each picture according to the M position coordinates, and determining the similarity of every two pictures in the N pictures according to the M-dimensional feature vector of each picture.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for determining similarity, comprising:
for each of N pictures, identifying M first objects and second objects in the picture, wherein the M first objects and the second objects are placed in a predetermined position in the picture, N is an integer greater than 1, and M is an integer greater than 0;
acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions;
and determining an M-dimensional feature vector of each picture according to the M position coordinates, and determining the similarity of every two pictures in the N pictures according to the M-dimensional feature vector of each picture.
2. The method of claim 1, wherein for each of the N pictures, before identifying the M first objects and the second objects in the picture, the method further comprises:
and shooting the M first objects and the second objects for N times at the same preset angle to obtain N pictures.
3. The method of claim 1, wherein obtaining M position coordinates of the first center positions relative to the second center positions of the M first objects comprises:
and sequentially taking M relative positions of the M first central positions relative to the second central position as the M position coordinates.
4. The method of claim 1, wherein determining similarity of two pictures in the N pictures according to the M-dimensional feature vector of each picture comprises:
and acquiring the distance of the feature vectors corresponding to any two pictures, and determining that the any two pictures are similar under the condition that the distance meets a preset condition.
5. A similarity determination device, comprising:
the identification module is used for identifying M first objects and second objects in the pictures for each of N pictures, wherein the M first objects and the second objects are placed in the pictures at preset positions, N is an integer larger than 1, and M is an integer larger than 0;
the acquisition module is used for acquiring first central positions of M first objects and second central positions of the second objects, and acquiring M position coordinates of the first central positions of the M first objects relative to the second central positions;
and the determining module is used for determining the M-dimensional characteristic vector of each picture according to the M position coordinates and determining the similarity of every two pictures in the N pictures according to the M-dimensional characteristic vector of each picture.
6. The apparatus of claim 5, wherein the recognition module is further configured to take N times the M first objects and the M second objects at the same preset angle, so as to obtain N pictures.
7. The apparatus of claim 5, wherein the obtaining module is configured to take M relative positions of the M first center positions relative to the second center position as the M position coordinates in turn.
8. The apparatus according to claim 5, wherein the determining module is further configured to obtain a distance between feature vectors corresponding to any two pictures, and determine that the any two pictures are similar when the distance satisfies a preset condition.
9. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the method of any one of claims 1 to 4 when executed.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 4.
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