CN114462533A - Clustering object clustering method - Google Patents

Abstract

The invention discloses a clustering method for clustered objects, which relates to the technical field of data mining and solves the technical problem that the existing clustering algorithm is not flexible enough. Initializing a cluster central point randomly; the clustering quantity does not need to be specified, and the use is more flexible; and distance degrees are introduced in the clustering process, and the distance of the relative distance of the object is reasonably analyzed.

Description

Clustering object clustering method

Technical Field

The application relates to the technical field of data mining, in particular to a clustering method for clustered objects.

Background

In some clustering tasks, attention needs to be paid to the dynamic of clustered targets and the correlation of similar targets, such as putting, pursuing, car accidents, etc., in which objects are not present by example, but a plurality of objects are present at the same time and have certain relation. When analyzing behaviors, attributes and states of these objects having a certain relation, the objects need to be combined according to a certain rule.

When the Kmeans clustering algorithm is used for solving the problem, the coordinates of the central points of all objects are generally clustered, the width and the height of the objects cannot be considered, and the clustering box at least needs to surround all the objects of the class, so that the maximum probability of surrounding the clustering box can contain the objects of other classes, and real clustering is not achieved.

Disclosure of Invention

The application provides a clustering object clustering method, which aims to make the clustering method more flexible, avoid initializing a clustering central point, introduce a distance degree in the clustering process and reasonably analyze the distance of relative distances of objects.

The technical purpose of the application is realized by the following technical scheme:

a clustered object clustering method, comprising:

step 1: acquiring a minimum bounding box set A of n objects, wherein A is { a ═ a }₁,a₂...,a_nGiving a search radius J at the same time; wherein n is>1；

Step 2: calculating the central point of each smallest enclosing frame in A as c₁,c₂,...,c_n；

And step 3: traversing and calculating the straight-line distance between the central point of each minimum surrounding frame and the central points of the rest smallest surrounding frames in the step A, and acquiring the minimum value of the straight-line distances between the central points of each minimum surrounding frame and the rest smallest surrounding frames, wherein the minimum value is d₁,d₂,…,d_nDividing the minimum value by the maximum value of the width and the height of the current minimum bounding box to obtain a distance b, wherein the calculation formula of b is as follows:

and 4, step 4: sorting the minimum bounding boxes in the A from small to large according to the corresponding b values to obtain a box set M, and setting M to { M ═ M₁,m₂,…,m_n}；

And 5: establishing a clustering frame empty set Q;

step 6: establish an empty set L and join m₁While M rejects M₁(ii) a Go through M, first with M₁Center point mc₁As a central point K, taking J as a search radius, finding all frames in the range, adding L, and then removing all frames meeting the conditions from M；

And 7: traversing the L, calculating the average value of the center points of all frames in the L, and updating the center point K; then repeating the step 6 until L is not updated; traversing the boundary coordinates of all frames in the L to obtain the minimum value and the maximum value of the boundary coordinates, wherein the minimum value and the maximum value are x respectively_min、y_min、x_max、y_maxIf so, the coordinate is the boundary coordinate of the clustering frame q;

and 8: and (3) calculating the area ratio iou of the rest frame and the clustering frame q in the M in a traversing way, wherein the area ratio iou is expressed as:

when frame m_nWhen the area ratio iou is not less than 0.8, the frame m is set_nAdding the obtained data into a clustering box q, and updating the boundary range of the clustering box q to completely surround m_nThen put the frame m_nRemoving the M, emptying the set L, and adding all the cluster frames into a set Q, wherein the finally obtained cluster frames are the final current clustering result;

wherein the inter represents a frame m_nArea intersection with the clustering box q, area_nA representation frame m_nThe area of (d);

and step 9: traversing the rest M, repeating the steps 6 to 8 until the M is empty, obtaining all clustering frames, and updating a set Q ═ Q₁,q₂,…,q_j}，j≤n。

The beneficial effect of this application lies in: according to the clustering object clustering method, more reasonable distance degree is introduced to judge the relative distance between objects, the dense center is used as an initial clustering center, surrounding adjacent objects are gradually merged, and the boundaries of clustering frames are updated. Initializing a cluster central point randomly; the clustering quantity does not need to be specified, and the use is more flexible; and distance degrees are introduced in the clustering process, and the distance of the relative distance of the object is reasonably analyzed.

Drawings

FIG. 1 is a flow chart of a method described herein;

FIG. 2 is a schematic diagram of the clustering effect of clustering by the method of the present application.

Detailed Description

The technical solution of the present application will be described in detail below with reference to the accompanying drawings.

Fig. 1 is a flow chart of a method according to the present application, as shown in fig. 1, the method comprising:

step 1: acquiring a minimum bounding box set A of n objects, wherein A is { a ═ a }₁,a₂...,a_nGiving a search radius J at the same time; wherein n is>1。

Step 2: calculating the central point of each smallest enclosing frame in A as c₁,c₂,...,c_n。

And step 3: traversing and calculating the straight-line distance between the central point of each minimum surrounding frame and the central points of the rest smallest surrounding frames in the step A, and acquiring the minimum value of the straight-line distances between the central points of each minimum surrounding frame and the rest smallest surrounding frames, wherein the minimum value is d₁,d₂,…,d_nDividing the minimum value by the maximum value of the width and the height of the current minimum bounding box to obtain a distance b, wherein the calculation formula of b is as follows:

and 4, step 4: sorting the minimum bounding boxes in the A from small to large according to the corresponding b values to obtain a box set M, and setting M to { M ═ M₁,m₂,…,m_n}。

And 5: and establishing a clustering frame empty set Q.

Step 6: establish an empty set L and join m₁While M rejects M₁(ii) a Go through M, first with M₁Center point mc₁And taking J as a search radius as a central point K, finding all frames in the range, adding L, and then removing all frames meeting the conditions from M.

And 7: traversing the L, calculating the average value of the center points of all frames in the L, and updating the center point K; then repeating the step 6 until L is not updated; traversing the boundary coordinates of all frames in L to obtain the minimum value and the maximum value of the boundary coordinates, wherein the minimum value and the maximum value are x respectively_min、y_min、x_max、y_maxAnd then the coordinate is the boundary coordinate of the clustering box q.

And 8: and (3) calculating the area ratio iou of the rest frame and the clustering frame q in the M in a traversing way, wherein the area ratio iou is expressed as:

when the frame m_nWhen the area ratio iou is not less than 0.8, the frame m is set_nAdding the obtained data into a clustering box q, and updating the boundary range of the clustering box q to completely surround m_nThen put the frame m_nRemoving the M, finally emptying the set L, and adding all the clustering frames into a set Q, wherein the finally obtained clustering frame is the final current clustering result;

wherein the inter represents a frame m_nArea intersection with the clustering box q, area_nA representation frame m_nThe area of (a).

And step 9: traversing the rest M, repeating the steps 6 to 8 until the M is empty, obtaining all clustering frames, and updating a set Q ═ Q₁,q₂,…,q_j}，j≤n。

The distance degree attribute of each clustering box Q in the clustering box set Q finally obtained in the step 9 is different m in the step 6₁The distance degree of (c). The distance degree represents the relative distance between objects. When a plurality of frames exist in one clustered frame, the distance degree of the clustered frame is the minimum value of the plurality of frames, namely the distance degree of m1, and the use is that if 10 frames are clustered, only 3 frames are needed, and 3 clustered frames are selected according to the distance degree.

And selecting the clustering frames according to the sequence, wherein the sequence of the clustering frames is the sequence of the relation distances among the objects from near to far, selecting the frames which are most likely to have aggregation and contact states according to the sequence, and selecting the first frames by self-definition and then accessing the subsequent algorithm processing or business.

Fig. 2 is a schematic diagram of a clustering effect of clustering by the method of the present application, where (a) in fig. 2 is an object frame before clustering, and (b) in fig. 2 is an object frame after clustering and converted into a square, and a number at the center of the frame indicates a distance degree of the frame. Converting the clustering frame into a square object frame, specifically comprising: keeping the central point of the clustering frame unchanged, and enabling the side length of the square to be equal to the maximum value of the width and the height of the clustering frame.

The foregoing is an exemplary embodiment of the method described herein, and the scope of protection is defined by the claims and their equivalents.

Claims (2)

1. A clustering method for clustered objects, comprising:

step 1: acquiring a minimum bounding box set A of n objects, wherein A is { a ═ a }₁,a₂...,a_nGiving a search radius J at the same time; wherein n is>1；

Step 2: calculating the central point of each smallest enclosing frame in A as c₁,c₂,...,c_n；

And step 3: traversing and calculating the straight-line distance between the central point of each minimum surrounding frame and the central points of the rest smallest surrounding frames in the step A, and acquiring the minimum value of the straight-line distances between the central points of each minimum surrounding frame and the rest smallest surrounding frames, wherein the minimum value is d₁,d₂,…,d_nDividing the minimum value by the maximum value of the width and the height of the current minimum bounding box to obtain a distance b, wherein the calculation formula of b is as follows:

and 4, step 4: sorting the minimum bounding boxes in the A from small to large according to the corresponding b values to obtain a box set M, and setting M to { M ═ M₁,m₂,…,m_n}；

And 5: establishing a clustering frame empty set Q;

step 6: establish an empty set L and join m₁While M rejects M₁(ii) a Go through M, first with M₁Center point mc₁Taking J as a search radius as a central point K, finding out all frames in the range, adding L, and then removing all frames meeting the conditions from M;

and 7: traversing the L, calculating the average value of the center points of all frames in the L, and updating the center point K;then repeating the step 6 until L is not updated; traversing the boundary coordinates of all frames in the L to obtain the minimum value and the maximum value of the boundary coordinates, wherein the minimum value and the maximum value are x respectively_min、y_min、x_max、y_maxIf so, the coordinate is the boundary coordinate of the clustering frame q;

and 8: and (3) calculating the area ratio iou of the rest frame and the clustering frame q in the M in a traversing way, wherein the area ratio iou is expressed as:

when the frame m_nWhen the area ratio iou is not less than 0.8, the frame m is set_nAdding the obtained data into a clustering box q, and updating the boundary range of the clustering box q to completely surround m_nThen put the frame m_nRemoving the M, emptying the set L, and adding all the cluster frames into a set Q, wherein the finally obtained cluster frames are the final current clustering result;

wherein the inter represents a frame m_nArea intersection with the clustering box q, area_nA representation frame m_nThe area of (d);

and step 9: traversing the rest M, repeating the steps 6 to 8 until the M is empty, obtaining all clustering frames, and updating a set Q ═ Q₁,q₂,…,q_j}，j≤n。

2. The clustering method according to claim 1, wherein converting the cluster boxes in the cluster box set Q into squares comprises: keeping the central point of the clustering frame unchanged, wherein the side length of the square is equal to the maximum value of the width and the height of the clustering frame.

Images