CN111401385B - Similarity calculation method for image local topological structure feature descriptors - Google Patents

Similarity calculation method for image local topological structure feature descriptors Download PDF

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CN111401385B
CN111401385B CN202010194853.9A CN202010194853A CN111401385B CN 111401385 B CN111401385 B CN 111401385B CN 202010194853 A CN202010194853 A CN 202010194853A CN 111401385 B CN111401385 B CN 111401385B
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桑强
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Abstract

The invention provides a similarity calculation method of an image local topological structure feature descriptor, firstly, a local topological structure feature descriptor is provided, secondly, when local similarity is compared, the corresponding relation of the local structures is found by two partial order structures based on the feature of the descriptor, and the difference between different local topological structures is quantified by the provided cost calculation method, so that the similarity of the topological structures can be effectively calculated. Compared with the existing method, the method accurately quantifies the difference of local structures.

Description

Similarity calculation method for image local topological structure feature descriptors
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a similarity calculation method for an image local topological structure feature descriptor.
Background
The local features of an image are image patterns, which measure the difference between different regions of the image, and points, edges or small image blocks can be used as the local features of the image. In general, a local image is defined as a small region around the feature center, local descriptors are generated based on context information of the region, and a numerical difference between the descriptors is calculated through a corresponding algorithm, so as to obtain a quantized feature difference. Local features have been widely used in computer vision systems as a powerful toolAnd multiple application scenarios. The point features are the most important image features due to good feature invariance and calculation real-time performance, particularly in SLAM and stereoscopic vision algorithms, whether local stability can be kept in a target matching process or not is the key influencing registration accuracy and final 3D modeling accuracy, and at the moment, the images are represented in a discrete point set mode through a feature point extraction algorithm. When calculating the local features of images, the currently mainstream local point feature calculation method is based on a shape context (shapecontext) and a similar histogram-based feature descriptor method, the shape context is mainly used for measuring the similarity of the contour shapes of two images, the statistical distribution characteristic of neighborhood points around a feature center point is utilized, and the shape features around the center point are described according to the angle and distance information of the distribution of the neighborhood points. Other similar methods include a contour point histogram (CPDH), which takes the center of the minimum circumscribed circle of the target object as the center point, forms a log-polar coordinate histogram according to the distribution of contour points, and uses an EMD (Earth Mover's Distance) Distance to calculate the similarity measure between two contour histograms when calculating the contour similarity. Most of recent algorithms, especially in 3D point cloud registration, shape context and similar methods are selected when calculating local features, however, these methods are mainly used to describe image contours, not local structures of targets, and shape context can describe local features of images to some extent. When describing a target local structure, due to the requirement of locality, sampling points are necessarily limited, usually several neighbor points around a central point, usually less than ten, are not enough to form an effective feature histogram, secondly, due to sparse histograms formed by too few feature points, local similarity cannot be effectively calculated, and when quantifying the similarity, the degree of similarity of different local features needs to be distinguished. But between calculating two histograms2In the case of distance, the calculated cost values are the same as long as the contour points are distributed on different grids of the histogram. For example,three equal-length sides with angles of 0 degree, 45 degrees and 90 degrees respectively, and X obtained by calculating the shape context of the three equal-length sides2The distances are the same, and in the general sense, it is considered that the 0 ° side is more similar to the 45 ° side, and the 90 ° side is more different, so that the method cannot accurately represent the degree of the profile difference.
Disclosure of Invention
In view of the above disadvantages in the prior art, the method for calculating the similarity of the image local topological structure feature descriptors provided by the present invention can effectively calculate the similarity of the topological structure, and accurately quantify the difference of the local structure compared with the existing method.
In order to achieve the above purpose, the invention adopts the technical scheme that:
the scheme provides a similarity calculation method for image local topological structure feature descriptors, which comprises the following steps:
s1, representing the image contour as a set of points SPAnd according to said points, collecting points piObtaining N points nearest to the midpoint from the neighborhood points, and forming a neighborhood structure of the image by the N points;
s2, obtaining an angle value and a distance value between the two points of the neighborhood point and the central point;
s3, respectively obtaining a distance sequence and an angle sequence of the neighborhood points around the central point according to the angle value and the distance value, and forming a feature descriptor of the image local topological structure by the distance sequence and the angle sequence;
s4, when comparing similarity, obtaining an initial corresponding relation of a distance sequence according to the distance sequence based on the feature descriptor, and obtaining a corresponding relation of angle sequence verification according to the angle sequence;
s5, judging whether the initial corresponding relation is consistent with the verification corresponding relation, if so, adding the consistent corresponding relation into the final corresponding relation z, and entering the step S6, otherwise, entering the step S7;
s6, calculating by using a cost calculation method according to the final corresponding relation z to obtain a matching probability P' when the initial corresponding relation is consistent with the verification corresponding relation;
s7, calculating by using a cost calculation method to obtain a matching probability P' when the initial corresponding relation is inconsistent with the verification corresponding relation;
s8, according to the matching probability P 'and the matching probability P' quantification structure difference, completing the similarity calculation of the image local topological structure feature descriptor.
Further, the feature descriptor in the step S3
Figure GDA0003561282990000031
The expression of (a) is as follows:
Figure GDA0003561282990000032
Figure GDA0003561282990000033
and is
Figure GDA0003561282990000034
Figure GDA0003561282990000035
And is
Figure GDA0003561282990000036
Wherein the content of the first and second substances,
Figure GDA0003561282990000037
a sequence of distances is represented which is,
Figure GDA0003561282990000038
is represented by piOne point, p, in the set of distance sequences formed for the feature centeriRepresents any feature center point on the image contour, L (-) represents the length distance between two points,
Figure GDA0003561282990000039
a sequence of angles is represented which is,
Figure GDA00035612829900000310
is represented by piFor one point in the set of angular sequences formed for the feature center, θ (-) represents the angular distance between the two points.
Still further, step S4 is specifically:
based on the feature descriptors, sequentially corresponding the distances of the neighborhood points in the distance sequence from small to large one by one to obtain an initial corresponding relation of the distance sequence; and
and based on the feature descriptors, sequentially corresponding the angles of the neighborhood points in the angle sequence from small to large one by one to obtain the initial corresponding relation of the angle sequence.
Still further, the structure difference S quantified in the step S6mnThe expression of (a) is as follows:
Smn＝P”+P'
Figure GDA0003561282990000041
Figure GDA0003561282990000042
Figure GDA0003561282990000043
wherein, P 'represents the matching probability when the initial corresponding relation is inconsistent with the verification corresponding relation, P' represents the matching probability when the initial corresponding relation is consistent with the verification corresponding relation, z represents the final corresponding relation, k represents that k points are matched in N neighborhood points, N represents the number of the neighborhood points, t represents the number of the neighborhood points, andirepresenting a certain point in a set, djRepresenting a point in another set, x being denoted by tiForming a certain point in the neighborhood for the centre point, y being denoted by djForming a certain point in the neighborhood for the centre point, tmThe representation and x form the points in the neighborhood point pair,
Figure GDA0003561282990000044
is represented by tmForming points x, d in the neighborhood for the center pointnThe representation and x form the points in the neighborhood point pair,
Figure GDA0003561282990000045
is represented by tmA point y in the neighborhood is formed for the center point,
Figure GDA0003561282990000046
represents tiThe neighborhood of points x of the image is,
Figure GDA0003561282990000047
denotes djNeighborhood point y, liRepresents tiPoint to point distance,/jDenotes djPair distance of points,/mRepresenting the distance, l, of all pairs of points computed and composed of the neighborhood points xnRepresenting the distance, θ, of all pairs of points computed and made up of the neighborhood points yiRepresents tiPoint to angle of (theta)jDenotes djDiagonal angle of points, θmRepresenting the angle, θ, of all pairs of points computed and made up of the neighborhood points xnRepresenting the angles of all pairs of points computed and made up of the neighborhood points y,
Figure GDA0003561282990000048
and
Figure GDA0003561282990000049
respectively, the maximum angle and distance values between the pairs of points, and C (-) represents the cost difference between the two pairs of points.
The invention has the beneficial effects that:
(1) the invention provides a new local structure feature descriptor, which consists of two sequences of feature point neighborhoods: one sequence describes the distance arrangement relation of the neighborhood, the other describes the angle arrangement relation of the neighborhood, and the two spatial relations accurately describe the stability of the local structure. Based on the stability, the method can accurately describe the local topological structure of the points, has good characteristic invariance, and is not influenced by the sparsity of the point set, high in accuracy and good in real-time particularly compared with the traditional histogram statistical distribution method;
(2) the invention provides a similarity calculation method based on the feature descriptors. When comparing the local similarity, the corresponding relation between two adjacent domains is obtained by the local stable relation described by the two sequences in the descriptor, wherein an initial corresponding relation can be obtained according to the distance sequence, a verification corresponding relation is obtained according to the angle sequence, if the two relations are consistent, the one-to-one corresponding relation between the adjacent domains can be determined, otherwise, the characteristics are not matched, and finally, the local difference of the characteristics is quantized by the corresponding relation through the proposed point-to-point calculation method. Because the similarity calculation is based on the local corresponding relation, the quantification of the similarity is more accurate, and different local areas of the image can be accurately compared;
(3) the feature descriptor integrates the context information of the image feature points, quantifies the difference degree between the local features of the image through the prior corresponding relation between the neighborhoods, and plays a key role in computer vision engineering as an image local feature quantification method.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Examples
As shown in fig. 1, the present invention provides a similarity calculation method for image local topology feature descriptors, which is implemented as follows:
s1, representing the image contour as a set of points SPAnd according to said points, collecting points piObtaining N points nearest to the midpoint from the neighborhood points, and forming a neighborhood structure of the image by the N points;
s2, obtaining an angle value and a distance value between the two points of the neighborhood point and the central point;
s3, respectively obtaining a distance sequence and an angle sequence of the neighborhood points around the central point according to the angle value and the distance value, and forming a feature descriptor of the image local topological structure by the distance sequence and the angle sequence;
s4, when comparing similarity, obtaining an initial corresponding relation of a distance sequence according to the distance sequence based on the feature descriptor, and obtaining a corresponding relation of angle sequence verification according to the angle sequence;
s5, judging whether the initial corresponding relation is consistent with the verification corresponding relation, if so, adding the consistent corresponding relation into the final corresponding relation z, and entering the step S6, otherwise, entering the step S7;
s6, calculating by using a cost calculation method according to the final corresponding relation z to obtain a matching probability P' when the initial corresponding relation is consistent with the verification corresponding relation;
s7, calculating by using a cost calculation method to obtain a matching probability P' when the initial corresponding relation is inconsistent with the verification corresponding relation;
s8, according to the matching probability P 'and the matching probability P' quantification structure difference, completing the similarity calculation of the image local topological structure feature descriptor.
In this embodiment, first an image is composed of sampling points on its contour, then a contour can be represented as a set of points SP＝p1,p2…,pMWhere M is the potential of the set of points. Point p in the setiIs a neighborhood of
Figure GDA0003561282990000071
Is defined as the nearest N points from the point, where o is 1,2,..., N. The local topological structure of the point is formed by the N points and can be a stable structure, the relation between the adjacent point and the central point is defined to be formed by an angle value and a distance value between the two points, the sum of the relation between the N point pairs uniquely determines the local topological structure of the central point, and the neighborhood points can form two ordered sets around the central point according to the size of the angle value and the distance value.
Figure GDA0003561282990000072
Wherein, satisfy
Figure GDA0003561282990000073
L (-) denotes the length distance between two points, where L is used2Distance. In the same way, the method for preparing the composite material,
Figure GDA0003561282990000074
wherein, satisfy
Figure GDA0003561282990000075
θ (-) represents the angular distance between two points. The new local structure descriptor proposed by the present invention is composed of these two ordered sets. For point piThe descriptor
Figure GDA0003561282990000076
In this embodiment, when the contour point set is rigidly deformed, the descriptor is kept consistent because the distance and angular relationship between the point pairs is not changed. When the contour points are elastically deformed, the point set is subjected to anisotropic changes, i.e., the changes of the distance between the point pairs and the angle value are arbitrary. However, it must be of some significance for the elastic deformation of the target object encountered in engineering applications. For example, the deformation of internal organs during respiration, the deformation of outline of animal walking, and the change of outline of different handwritten Chinese characters. Although the shape change of the different objects is elastic deformation, the change follows a certain rule, namely the inherent meaning of the object after the elastic deformationThere is no change, i.e. the heart changes its shape during respiration changes, but its contour features still indicate that it belongs to a heart organ or a cheetah during the progression, and the changing shape features can still be recognized at a glance as a cheetah. If the deformation is random, the target can not be identified completely, and the subsequent application value is lost, and no significance is realized. Therefore, based on the idea of differentiation, the image contour as a whole undergoes elastic deformation, i.e., changes in anisotropy; locally, the variation between points is relatively smooth, approximating an isotropic variation, with the variation in angle and length between a point and its neighbours being uniform. That is, the arrangement sequence of the neighborhood points around the central point is stable, and after the whole contour is elastically deformed, the local points TpiThe two ordered sets in the topology descriptor of (1) are stable and invariant.
In this embodiment, when calculating the similarity between two local structures, if the distributions of the neighboring points are completely the same, we can consider that the similarity between the two local structures is the largest, and conversely, the similarity is the smallest. When the number of the neighborhood points is equal to one (N is 1), the local structure cost difference can be obtained only by calculating the angle difference and the distance difference between two point pairs, because only one neighborhood point is a corresponding point, and when N is equal to>When 2, the key point is how to determine the correspondence between the local structure neighborhood points. The invention solves the corresponding relation between local structures according to the topology descriptors described above, because the local neighborhood point sequences are kept stable when the point sequences of two sets in the descriptors are elastically deformed in the whole point set, for two unknown local structures, the corresponding two descriptors are used
Figure GDA0003561282990000081
And
Figure GDA0003561282990000082
the determined correspondences are equally stable to the sequence. First, according to the descriptor
Figure GDA0003561282990000083
In (1)
Figure GDA0003561282990000084
And
Figure GDA0003561282990000085
in (1)
Figure GDA0003561282990000086
The corresponding relation between the two neighborhood point sets obtained by sorting according to the distance value can be obtained. Secondly, according to
Figure GDA0003561282990000087
In (1)
Figure GDA0003561282990000088
And
Figure GDA0003561282990000089
in
Figure GDA00035612829900000810
The corresponding relation between two neighborhood point sets correspondingly obtained according to the sequence of the sorting of the angle values can be obtained, namely, the correspondence is one-to-one according to the sequence of the distance of the neighborhood points and the angle values from small to large, namely
Figure GDA00035612829900000811
Corresponding to a point
Figure GDA00035612829900000812
Or is provided with
Figure GDA00035612829900000813
Corresponding to a point
Figure GDA00035612829900000814
And the two obtained corresponding relations are respectively recorded as: z is a radical oflAnd zθIf two points are at zlAnd zθAre consistent, then the two points are marked as corresponding points if all the points in the two local structures satisfyThe above correspondence relationship may be considered that the two local structures are completely similar, otherwise they are not similar or partially similar. Let k points in the partial structure correspond to each other, and the correspondence is taken as z. And meanwhile, N-k points are not matched, and for the matched point pairs, the probability of the quantized local structure similarity is given as follows:
Figure GDA00035612829900000815
for a point pair that is an unmatched point pair, the matching probability is:
Figure GDA0003561282990000091
here, C (·) is the cost difference between two pairs of points:
Figure GDA0003561282990000092
in the above formula, the first and second carbon atoms are,
Figure GDA0003561282990000093
and
Figure GDA0003561282990000094
respectively representing the maximum distance and angle values between the point pairs, and the final similarity calculation probability is as follows: smn＝P”+P'。
In summary, the present invention proposes a new local structure descriptor, which is composed of two sequences of feature point neighborhoods: one sequence describes the distance arrangement relation of the neighborhoods, the other describes the angle arrangement relation of the neighborhoods, the two spatial relations accurately describe the stability of the local structure, and then the corresponding relation between the two neighborhoods can be obtained based on the local stability relation described by the two sequences, wherein an initial corresponding relation can be obtained according to the distance sequence, a verification corresponding relation can be obtained according to the angle sequence, and if the two relations are consistent, the corresponding relation between the neighborhoods is obtained. Otherwise, the features are not matched, and finally, the quantized structural difference is calculated according to the obtained corresponding relation.

Claims (4)

1. A similarity calculation method for image local topological structure feature descriptors is characterized by comprising the following steps:
s1, representing the image contour as a point set SPAnd according to said points, collecting points piObtaining N points nearest to the midpoint from the neighborhood points, and forming a neighborhood structure of the image by the N points;
s2, obtaining an angle value and a distance value between the two points of the neighborhood point and the central point;
s3, respectively obtaining a distance sequence and an angle sequence of the neighborhood points around the central point according to the angle value and the distance value, and forming a feature descriptor of the image local topological structure by the distance sequence and the angle sequence;
s4, when comparing the similarity, based on the feature descriptor, obtaining the initial corresponding relation of the distance sequence according to the distance sequence, and obtaining the verification corresponding relation of the angle sequence according to the angle sequence;
s5, judging whether the initial corresponding relation is consistent with the verification corresponding relation, if so, adding the consistent corresponding relation into the final corresponding relation z, and entering the step S6, otherwise, entering the step S7;
s6, calculating by using a cost calculation method according to the final corresponding relation z to obtain a matching probability P' when the initial corresponding relation is consistent with the verification corresponding relation;
s7, calculating by using a cost calculation method to obtain a matching probability P' when the initial corresponding relation is inconsistent with the verification corresponding relation;
s8, according to the matching probability P 'and the matching probability P' quantification structure difference, completing the similarity calculation of the image local topological structure feature descriptor.
2. The method for calculating similarity of image local topological structure feature descriptors according to claim 1, wherein said feature descriptors in step S3
Figure FDA0003561282980000011
The expression of (c) is as follows:
Figure FDA0003561282980000012
Figure FDA0003561282980000013
and is provided with
Figure FDA0003561282980000014
Figure FDA0003561282980000015
And is
Figure FDA0003561282980000016
Wherein the content of the first and second substances,
Figure FDA0003561282980000021
a sequence of distances is represented which is,
Figure FDA0003561282980000022
is represented by piOne point of the set of distance sequences formed for the feature center, piRepresents any feature center point on the image contour, L (-) represents the length distance between two points,
Figure FDA0003561282980000023
a sequence of angles is represented which is,
Figure FDA0003561282980000024
is represented by piFor one point in the set of angular sequences formed for the feature center, θ (-) represents the angular distance between the two points.
3. The method for calculating the similarity of the image local topological structure feature descriptors according to claim 1, wherein the step S4 specifically includes:
based on the feature descriptors, sequentially corresponding the distances of the neighborhood points in the distance sequence from small to large one by one to obtain an initial corresponding relation of the distance sequence; and
and based on the feature descriptors, sequentially corresponding the angles of the neighborhood points in the angle sequence from small to large one by one to obtain the initial corresponding relation of the angle sequence.
4. The method for calculating similarity of image local topological structure feature descriptors according to claim 1, wherein said structure difference S quantified in step S8 is SmnThe expression of (a) is as follows:
Smn＝P”+P'
Figure FDA0003561282980000025
Figure FDA0003561282980000026
Figure FDA0003561282980000027
wherein, P 'represents the matching probability when the initial corresponding relation is inconsistent with the verification corresponding relation, P' represents the matching probability when the initial corresponding relation is consistent with the verification corresponding relation, z represents the final corresponding relation, k represents that k points in N neighborhood points are matched, N represents the number of the neighborhood points, t represents the matching probability when the initial corresponding relation is inconsistent with the verification corresponding relation, k represents the matching probability when the initial corresponding relation is consistent with the verification corresponding relation, k represents that k points in the N neighborhood points are matched, andirepresenting in a collectionA certain point, djRepresenting a point in another set, x being denoted by tiForming a certain point in the neighborhood for the center point, y being denoted by djForming a certain point in the neighborhood for the central point, tmThe representation and x form the points in the neighborhood point pair,
Figure FDA0003561282980000031
is represented by tmForming points x, d in the neighborhood for the center pointnThe representation and x form the points in the neighborhood point pair,
Figure FDA0003561282980000032
is represented by tmA point y in the neighborhood is formed for the center point,
Figure FDA0003561282980000033
denotes tiThe neighborhood of points x of the image data,
Figure FDA0003561282980000034
denotes djNeighborhood point y, liRepresents tiPoint to point distance,/jDenotes djPair distance of points,/mRepresenting the distance, l, of all pairs of points computed and composed of the neighborhood points xnRepresenting the distance, θ, of all pairs of points computed and made up of the neighborhood points yiRepresents tiPoint to angle of (theta)jDenotes djDiagonal of the point, θmRepresenting the angle, θ, of all pairs of points computed and made up of the neighborhood points xnRepresenting the angles of all pairs of points computed and made up of the neighborhood points y,
Figure FDA0003561282980000035
and
Figure FDA0003561282980000036
respectively, the maximum angle and distance values between the pairs of points, and C (-) represents the cost difference between the two pairs of points.
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