CN112464010B - Automatic image labeling method based on Bayesian network and classifier chain - Google Patents

Automatic image labeling method based on Bayesian network and classifier chain Download PDF

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CN112464010B
CN112464010B CN202011493104.2A CN202011493104A CN112464010B CN 112464010 B CN112464010 B CN 112464010B CN 202011493104 A CN202011493104 A CN 202011493104A CN 112464010 B CN112464010 B CN 112464010B
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王振武
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China University of Mining and Technology Beijing CUMTB
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    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
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    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
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Abstract

The invention discloses an image automatic labeling method based on a Bayesian network and a classifier chain, which is characterized in that a Bayesian network structure is learned by utilizing an improved BIC scoring function method, labels are clustered through a DBSCAN algorithm, a Bayesian network is learned for each label subset, feature selection is carried out through the Bayesian network between the labels and features, the classifier chain is constructed according to the topological sequence of the Bayesian network, and an image prediction label set is constructed through the Bayesian network and the classifier chain algorithm, so that all types of images can be labeled, and the method is strong in universality; meanwhile, the method can process the image containing continuous features and discrete features, has good adaptability, and effectively improves the robustness and accuracy of image labeling.

Description

Automatic image labeling method based on Bayesian network and classifier chain
Technical Field
The invention relates to the technical field of image retrieval, in particular to an automatic image labeling method based on a Bayesian network and a classifier chain.
Background
With the gradual development of technologies such as multimedia and image information, the image database is becoming larger and larger, which makes the management of visual information important, and the image retrieval technology can play a role in visual information management. The traditional manual image labeling method has large workload, and the subjectivity and the inaccuracy are inevitably brought, so that the automatic image labeling of a computer is imperative. The automatic image annotation is to make the computer automatically add the semantic keywords capable of reflecting the content of the image to the image, and the use of the automatic annotation can effectively improve the difficulty of the current image retrieval. The Bayesian network algorithm is a common probability graph model, correlation among the obtained labels is fully considered, and the classifier chain algorithm is a model for fully utilizing the correlation among the labels, so that how to provide an automatic image labeling method based on the Bayesian network and the classifier chain is a technical problem to be solved urgently at present.
Disclosure of Invention
The invention aims to provide an automatic image labeling method based on a Bayesian network and a classifier chain, which aims to solve the technical problems in the prior art, can label images of all types, has strong universality and adaptability, and effectively improves the robustness and accuracy of automatic image labeling.
In order to achieve the purpose, the invention provides the following scheme: the invention provides an automatic image labeling method based on a Bayesian network and a classifier chain, which comprises the following steps:
s1, obtaining a sample image, extracting the characteristics of the sample image to form a training set and a test set, obtaining the label of the sample image, and constructing a total label set;
s2, normalizing the characteristics of the sample images in the training set and the test set;
step S3, constructing a Bayesian network through a score searching method of an improved Bayesian information criterion BIC score function based on each label in the total label set and the characteristics of the sample image after normalization processing, and performing characteristic selection through the Bayesian network to obtain a characteristic subset corresponding to each label;
step S4, based on the feature subset corresponding to each label, clustering the labels in the total label set by adopting density clustering DBSCAN to generate a label subset;
step S5, respectively constructing a Bayesian network structure for each label subset based on the improved BIC scoring function scoring search method;
step S6, extracting a topological sequence of the Bayesian network structure constructed by each label subset, and constructing a classifier chain based on the topological sequence; and training and testing each base classifier in the classifier chain through the training set and the testing set to obtain the trained classifier chain, and performing class prediction on the image to be tested through the trained classifier chain to finish automatic labeling of the image.
Preferably, in step S3, each label l is assignedqConstruction of a Bayesian network
Figure GDA0003156813000000021
Wherein f iswwIn order to provide an improved scoring function,
Figure GDA0003156813000000022
on datasets for Bayesian network G
Figure GDA0003156813000000023
The value of the score function to be given,
Figure GDA0003156813000000031
is meant to make
Figure GDA0003156813000000032
A maximum bayesian network; finally, each label l is obtainedqCorresponding feature subset
Figure GDA0003156813000000033
d＝1，2，…，Dq，DqIs a label lqThe number of features of the corresponding feature subset.
Preferably, in the step S3, the solution is performed by a hill climbing method so that
Figure GDA0003156813000000034
The largest network structure.
Preferably, the step S5 specifically includes:
according to the scoring function in the step S3, nodes representing labels are continuously added in the initial Bayesian network;
randomly selecting a label as a starting point of mountain climbing search;
and constructing the Bayesian network structure by adding edges, subtracting edges or overturning.
Preferably, in the process of constructing the bayesian network structure, a condition of maximizing a scoring function is satisfied, and the bayesian network structure corresponding to each tag subset is obtained.
Preferably, in step S6, the training of each base classifier in the classifier chain through the training set includes:
based on each tag subset Lr(r＝1,2,…s) corresponding Bayesian network, constructing a tag dependent dictionary dependency _ factr＝{<keyq,valueq>}，keyqValue for the q-th tag in the subset of tagsqA parent node set of the q label in the label subset; relying tags on keys in a dictionaryqCorresponding feature subset and valueqAnd splicing to form a new feature set, and finishing the training of the base classifier.
Preferably, the base classifier employs a logistic regression model.
Preferably, in step S6, the method for performing class prediction on an image to be detected by using a trained classifier chain includes:
inputting the characteristics of each image to be detected into a base classifier corresponding to the non-precursor node label to obtain a prediction result; and inputting the prediction result into other base classifiers of the classifier chain, and integrating all output sets into a final image prediction result set to finish automatic annotation of the image.
The invention discloses the following technical effects:
the method learns the Bayesian network structure by using an improved BIC scoring function method, clusters the labels by using a DBSCAN algorithm, learns the Bayesian network for each label subset, selects the characteristics by using the Bayesian network among the labels and the characteristics, constructs a classifier chain according to the topological sequence of the Bayesian network, constructs an image prediction label set by using the Bayesian network and the classifier chain algorithm, and has strong universality, wherein all types of images can be labeled; meanwhile, the method can process the image containing continuous features and discrete features, has good adaptability, and effectively improves the robustness and accuracy of image labeling.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flowchart of an automatic image annotation method based on a Bayesian network and a classifier chain according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1, the present embodiment provides an automatic image annotation method based on a bayesian network and a classifier chain, which specifically includes the following steps:
s1, obtaining a sample image, extracting the characteristics of the sample image to form a training set and a test set, obtaining the label of the sample image, and constructing a total label set;
in this embodiment, the training set and the test set are respectively expressed as:
Figure GDA0003156813000000051
wherein m is the number of samples in the training set, n is the number of samples in the testing set, i is the image number,
Figure GDA0003156813000000052
for the ith image xiD is the total number of features,
Figure GDA0003156813000000053
representing the ith image xiThe D-th feature of (1, 2., D);
Figure GDA0003156813000000054
for the ith image xiThe corresponding label vector of the label set of (a),
Figure GDA0003156813000000055
q＝1，2，…，Q，
Figure GDA0003156813000000056
L＝{l1，l2，…，lQas the total label set, lqQ is the Q-th tag in L, and Q is the total number of tags.
S2, normalizing the characteristics of the sample images in the training set and the test set;
in this example, the normalization process is shown as follows:
Figure GDA0003156813000000061
in the formula (I), the compound is shown in the specification,
Figure GDA0003156813000000062
respectively representing the d-th feature x of the image xdMaximum and minimum values of, xd：normRepresenting the d-th feature x of the image xdThe result of normalization.
Step S3, based on each label l in the total label setqAnd the characteristic x of the normalized sample imaged：normThe Bayesian network G is constructed by the improved score search method of the BIC (Bayesian Information Criterion) score functionqThrough a Bayesian network GqSelecting features to obtain a feature subset corresponding to each label;
in this embodiment, each label l isqConstruction of a Bayesian network
Figure GDA0003156813000000063
For representing a label lqAnd the relationship between the characteristic variables; wherein f iswwIn order to provide an improved scoring function,
Figure GDA0003156813000000064
on datasets for Bayesian network G
Figure GDA0003156813000000065
The value of the score function to be given,
Figure GDA0003156813000000066
refer to all possible Bayesian networks such that
Figure GDA0003156813000000067
A maximum bayesian network; by extracting the network GqGet each label/qCorresponding feature subset
Figure GDA00031568130000000610
d＝1，2，…，Dq，DqIs a label lqThe number of features of the corresponding feature subset.
The specific method for constructing the Bayesian network structure based on the score search method of the improved BIC score function comprises the following steps:
s3-1, definition
Figure GDA0003156813000000068
Wherein the content of the first and second substances,
Figure GDA0003156813000000069
t represents the number of nodes in the Bayesian network, JtIs node NtNumber of parent node, KtIs node Ntη is a regulation parameter, in this embodiment, η is 10, m is the number of samples in the training set, UtIs node NtNumber of parent node, counttjkRepresenting a data set
Figure GDA0003156813000000071
Middle node NtK and the parent node state quantity is j,
Figure GDA0003156813000000072
Figure GDA0003156813000000073
represents NtAnd u, normalized mutual information quantity;
Figure GDA0003156813000000074
represents NtAnd u, H () represents the solution information entropy, and p () represents the solution probability.
S3-2, adopting hill climbing method to obtain the optimum structure of all Bayesian network structures
Figure GDA0003156813000000075
Step S4, Based on the feature subset corresponding to each label, Clustering the labels in the total label set by using Density-Based Clustering of Applications with Noise (Density-Based Clustering method) to generate a label subset L1，L2，…，LsAnd s is the number of tag subsets.
Step S5, respectively, the score searching method based on the improved BIC score function is used for each label subset Lr(r ═ 1,2, … s) to construct a bayesian network structure Gr(ii) a The method specifically comprises the following steps:
according to the scoring function defined in the step S3-1, nodes representing labels are continuously added in an initial network, wherein the initial network is a disconnected empty network;
selecting a label lq(Q1, 2, …, Q) as the starting point for hill climbing search to ensure that there must be a label l in the networkq(because the number of features is huge, and the hill-climbing search stops searching when the increment of the scoring function is less than 1e-8, the network between the tags and the features only contains partial features); wherein Q is the total number of the tags; the Bayesian network structure is constructed by adding edges, subtracting edges or turning, and the characteristic nodes contained in the constructed Bayesian network structure are the characteristic nodesIs a label lqCorresponding feature subset
Figure GDA0003156813000000081
d＝1，2，…，Dq，DqIs a label lqThe number of features of the corresponding feature subset; and in the construction process of the network structure, the maximization of the scoring function is met, and the Bayesian network structure is obtained.
Step S6, Bayesian network structure G constructed for each label subsetrExtracting a topological sequence, and constructing a classifier chain based on the topological sequence; and training and testing each base classifier in the classifier chain through the training set and the testing set to obtain the trained classifier chain, and performing class prediction on the image to be tested through the trained classifier chain to finish automatic labeling of the image.
Parsing each subset of labels L in step S5r(r ═ 1,2, … s) corresponding bayesian network structure GrConstructing a tag dependent dictionary dependency _ factr＝{<keyq,valueq>}，r＝1,2,…s,q＝1,2,…Qr,QrIs a subset L of tagsrNumber of tags owned, keyqValue for the q-th tag in the subset of tagsqA parent node set of the q label in the label subset; since some tags do not have a parent tag (root node in the tag network), such tags do not have a tag that needs to be relied upon, with value null.
The process of training each base classifier in the classifier chain through the training set includes:
each subset of tags Lr(r ═ 1,2, … s) are all given the label dependent dictionary dependency _ factr＝{<keyq,valueq>}; for each key in the tag-dependent dictionary, its corresponding feature subset
Figure GDA0003156813000000082
d＝1，2，…，DqAnd its dependency _ factrValue (l) ofq1，lq2，...，lqn) Carry out the splicingForming a new feature set; wherein q isnFor the number of tags in value, tag lqTraining a base classifier for each key as a prediction target; the base classifier uses a logistic regression model, and in this embodiment, the classification threshold is 0.5.
The method for carrying out category prediction on the image to be detected through the trained classifier chain comprises the following steps:
inputting the characteristics of each image to be detected into a base classifier corresponding to the non-precursor node label to obtain a prediction result; and inputting the prediction result into other corresponding base classifiers, and synthesizing all output sets into a final image prediction result set to finish automatic annotation of the image.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.

Claims (7)

1. An automatic image labeling method based on a Bayesian network and a classifier chain is characterized by comprising the following steps:
s1, obtaining a sample image, extracting the characteristics of the sample image to form a training set and a test set, obtaining the label of the sample image, and constructing a total label set;
s2, normalizing the characteristics of the sample images in the training set and the test set;
step S3, constructing a Bayesian network through a score searching method of an improved Bayesian information criterion BIC score function based on each label in the total label set and the characteristics of the sample image after normalization processing, and performing characteristic selection through the Bayesian network to obtain a characteristic subset corresponding to each label;
step S4, based on the feature subset corresponding to each label, clustering the labels in the total label set by adopting density clustering DBSCAN to generate a label subset;
step S5, respectively constructing a Bayesian network structure for each label subset based on the improved BIC scoring function scoring search method;
step S6, extracting a topological sequence of the Bayesian network structure constructed by each label subset, and constructing a classifier chain based on the topological sequence; training and testing each base classifier in the classifier chain through a training set and a testing set to obtain a trained classifier chain, and performing class prediction on an image to be tested through the trained classifier chain to finish automatic labeling of the image;
in the step S3, each label l is assignedqConstruction of a Bayesian network
Figure FDA0003156812990000011
Wherein f iswwIn order to provide an improved scoring function,
Figure FDA0003156812990000012
on datasets for Bayesian network G
Figure FDA0003156812990000021
The value of the score function to be given,
Figure FDA0003156812990000022
is meant to make
Figure FDA0003156812990000023
A maximum bayesian network; finally, each label l is obtainedqCorresponding feature subset
Figure FDA0003156812990000024
d＝1，2，…，Dq，DqIs a label lqThe number of features of the corresponding feature subset;
Figure FDA0003156812990000025
wherein the content of the first and second substances,
Figure FDA0003156812990000026
t represents the number of nodes in the Bayesian network, JtIs node NtNumber of parent node, KtIs node NtIs the number of state variables, eta is the tuning parameter, m is the number of samples in the training set, UtIs node NtNumber of parent node, counttjkRepresenting a data set
Figure FDA0003156812990000027
Middle node NtK and the parent node state quantity is j,
Figure FDA0003156812990000028
represents NtAnd u, normalized mutual information quantity;
Figure FDA0003156812990000029
represents NtAnd u, H () represents the solution information entropy, and p () represents the solution probability.
2. The Bayesian network and classifier chain-based image automatic labeling method as claimed in claim 1, wherein in step S3, solving is performed by hill climbing so that
Figure FDA00031568129900000210
The largest network structure.
3. The automatic image annotation method based on the bayesian network and the classifier chain as claimed in claim 2, wherein said step S5 specifically comprises:
according to the scoring function in the step S3, nodes representing labels are continuously added in the initial Bayesian network;
randomly selecting a label as a starting point of mountain climbing search;
and constructing the Bayesian network structure by adding edges, subtracting edges or overturning.
4. The Bayesian network and classifier chain-based image automatic labeling method as claimed in claim 3, wherein in the Bayesian network structure construction process, a condition of maximizing a scoring function is satisfied, and a Bayesian network structure corresponding to each tag subset is obtained.
5. The Bayesian network and classifier chain-based image automatic labeling method of claim 3, wherein in the step S6, the training of each base classifier in the classifier chain through the training set comprises:
based on each tag subset Lr(r ═ 1,2,. s) corresponding bayesian network, constructing a tag dependency dictionary dependency _ factr＝{＜keyq，valueq＞}，keyqValue for the q-th tag in the subset of tagsqA parent node set of the q label in the label subset; relying tags on keys in a dictionaryqCorresponding feature subset and valueqAnd splicing to form a new feature set, and finishing the training of the base classifier.
6. The Bayesian network and classifier chain based image automatic labeling method of claim 5, wherein the base classifier employs a logistic regression model.
7. The Bayesian network and classifier chain-based image automatic labeling method of claim 5, wherein in the step S6, the method for performing class prediction on the image to be tested through the trained classifier chain comprises:
inputting the characteristics of each image to be detected into a base classifier corresponding to the non-precursor node label to obtain a prediction result; and inputting the prediction result into other base classifiers of the classifier chain, and integrating all output sets into a final image prediction result set to finish automatic annotation of the image.
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