阅读说明：本技术 一种鲁棒二值属性学习方法及系统 (Robust binary attribute learning method and system ) 是由 于治楼 袭肖明 于 2019-10-28 设计创作，主要内容包括：本发明公开了一种鲁棒二值属性学习方法及系统,属于计算机视觉技术领域。本发明的鲁棒二值属性学习方法,将每个二值码作为一个属性,表示目标的物理特征,构建二值属性学习器,并根据构建的二值属性学习期来构建关联二值码特征学习器,直接学习多个相关的二值属性,获得多维二值码特征,融合多个二值码属性和关联多维二值码,将待测试图像输入到二值属性学习器和关联二值码特征学习器,获得该图像的辨别性二值码特征表示,将辨别性二值码特征表示与注册的二值码模板比对,基于相似度判断辨别性二值码与注册的二值码是否为同一类。该发明的鲁棒二值属性学习方法具有较好的理解性和鲁棒性,能够有望提高识别精度和效率,具有很好的推广应用价值。(The invention discloses a robust binary attribute learning method and a robust binary attribute learning system, and belongs to the technical field of computer vision. The robust binary attribute learning method comprises the steps of taking each binary code as an attribute, representing physical characteristics of a target, constructing a binary attribute learner, constructing a related binary code characteristic learner according to a constructed binary attribute learning period, directly learning a plurality of related binary attributes, obtaining multi-dimensional binary code characteristics, fusing a plurality of binary code attributes and related multi-dimensional binary codes, inputting an image to be tested into the binary attribute learner and the related binary code characteristic learner, obtaining discriminative binary code characteristic representation of the image, comparing the discriminative binary code characteristic representation with a registered binary code template, and judging whether the discriminative binary code and the registered binary code are in the same class or not based on similarity. The robust binary attribute learning method has better comprehensiveness and robustness, can hopefully improve the identification precision and efficiency, and has good popularization and application values.)

1. A robust binary attribute learning method is characterized in that: in the method, each binary code is used as an attribute to represent physical characteristics of a target, a binary attribute learning device is constructed, an associated binary code characteristic learning device is constructed according to a constructed binary attribute learning period, a plurality of relevant binary attributes are directly learned to obtain a multi-dimensional binary code characteristic, a plurality of binary code attributes and an associated multi-dimensional binary code are fused, an image to be tested is input into the binary attribute learning device and the associated binary code characteristic learning device to obtain a discriminative binary code characteristic representation of the image, the discriminative binary code characteristic representation is compared with a registered binary code template to obtain similarity of the two, and whether the discriminative binary code and the registered binary code are in the same class or not is judged based on the similarity.

2. The robust binary attribute learning method of claim 1, wherein: the method specifically comprises the following steps:

s1, training phase

1) Constructing a binary attribute learning device;

2) constructing a relevant binary code learner;

and S2, in the testing stage, respectively inputting the image to be tested into the binary attribute learning device and the associated binary code learning device to obtain the discriminative binary code feature representation of the image, comparing the discriminative binary code feature representation with the registered binary code template to obtain the similarity of the discriminative binary code feature representation and the registered binary code template, and judging whether the discriminative binary code and the registered binary code are in the same class or not based on the similarity.

3. The robust binary attribute learning method of claim 2, wherein: in the process of constructing the binary attribute learner, the binary attributes of the target to be recognized are obtained based on the cognitive priori knowledge of the target in the sample, the sample is divided into a positive class and a negative class according to the value of each attribute, and training is carried out by utilizing the densenet based on the collected sample and the attribute mark to obtain the binary attribute learner.

4. The robust binary attribute learning method of claim 3, wherein: constructing a correlation binary code learner by utilizing a convolutional neural network learning framework, introducing a correlation loss function into a loss layer by adopting a densenert in a network framework, obtaining the correlation binary code learner by minimizing an objective function as shown in a formula (1),

where t is the number of binary attributes, Y _(i，k)For the value of the kth attribute of all samples, X is the input image, is a matrix of nxd, W _kIs the associated weight vector, and is a hyperparameter.

5. The robust binary attribute learning method of claim 4, wherein: in the testing stage, an image to be tested is firstly respectively input into the binary attribute learning device and the associated binary code learning device to obtain discriminative binary code feature representation of the image, the discriminative binary code feature representation is compared with a registered binary code template, the similarity between the discriminative binary code feature representation and the registered binary code template is obtained by sea name distance measurement, and whether the discriminative binary code and the registered binary code are in the same class or not is judged based on the similarity.

6. A robust binary attribute learning system, characterized by: the system comprises a training module and a testing module:

the training module is used for constructing a binary attribute learning device and an associated binary code learning device in a training stage;

the test module is used for respectively inputting the image to be tested into the binary attribute learning device and the associated binary code learning device in the test stage, obtaining the discriminative binary code feature representation of the image, comparing the discriminative binary code feature representation with the registered binary code template to obtain the similarity of the discriminative binary code feature representation and the registered binary code template, and judging whether the discriminative binary code and the registered binary code are in the same class or not based on the similarity.

7. The robust binary attribute learning system of claim 6, wherein: in the process of constructing the binary attribute learner, the binary attributes of the target to be recognized are obtained based on the cognitive priori knowledge of the target in the sample, the sample is divided into a positive class and a negative class according to the value of each attribute, training is carried out by using a densenet based on the collected sample and the attribute marks, and the binary attribute learner is obtained.

8. The robust binary attribute learning system of claim 7, wherein: in the training module, a convolutional neural network learning framework is utilized to construct a correlated binary code learner, a network architecture adopts densenet, a correlated loss function is introduced into a loss layer, as shown in a formula (1), the correlated binary code learner is obtained by minimizing an objective function,

where t is the number of binary attributes, Y _(i，k)For the value of the kth attribute of all samples, X is the input image, is a matrix of nxd, W _kIs the associated weight vector, and is a hyperparameter.

9. The robust binary attribute learning system of claim 7, wherein: the test module firstly inputs an image to be tested into the binary attribute learning device and the associated binary code learning device respectively in a test stage, discriminative binary code feature representation of the image is obtained, the discriminative binary code feature representation is compared with a registered binary code template, the similarity between the discriminative binary code feature representation and the registered binary code template is obtained by sea distance measurement, and whether the discriminative binary code and the registered binary code are in the same class or not is judged based on the similarity.

Technical Field

The invention relates to the technical field of computer vision, and particularly provides a robust binary attribute learning method and system.

Background

Binary codes have the advantages of easy storage, high calculation efficiency and the like. In the field of computer vision, an important application of binary codes is to represent attribute features as targets. However, the traditional binary code features have poor understandability and do not have good robustness. How to effectively solve the problems of poor understandability, poor robustness and the like of the existing binary code characteristics has important research significance and application value. Aiming at the problems of the existing binary code method, the invention provides an intuitive robust binary code learning method. The proposed binary code features have better comprehensibility and robustness, and are expected to improve the identification precision and efficiency.

Disclosure of Invention

The technical task of the invention is to provide a robust binary attribute learning method which has better comprehension and robustness and can hopefully improve the identification precision and efficiency aiming at the problems.

A further technical task of the present invention is to provide a robust binary attribute learning system.

In order to achieve the purpose, the invention provides the following technical scheme:

a robust binary attribute learning method includes the steps of enabling each binary code to serve as an attribute, representing physical characteristics of a target, constructing a binary attribute learning device, constructing a related binary code characteristic learning device according to a constructed binary attribute learning period, directly learning a plurality of related binary attributes, obtaining multi-dimensional binary code characteristics, fusing a plurality of binary code attributes and related multi-dimensional binary codes, inputting an image to be tested into the binary attribute learning device and the related binary code characteristic learning device, obtaining discriminative binary code characteristic representation of the image, comparing the discriminative binary code characteristic representation with a registered binary code template to obtain similarity of the two, and judging whether the discriminative binary code and the registered binary code are the same type or not based on the similarity.

The robust binary attribute learning method has better comprehensiveness and robustness, and can hopefully improve the identification precision and efficiency.

Preferably, the robust binary attribute learning method specifically includes the following steps:

s1, training phase

1) Constructing a binary attribute learning device;

2) constructing a relevant binary code learner;

and S2, in the testing stage, respectively inputting the image to be tested into the binary attribute learning device and the associated binary code learning device to obtain the discriminative binary code feature representation of the image, comparing the discriminative binary code feature representation with the registered binary code template to obtain the similarity of the discriminative binary code feature representation and the registered binary code template, and judging whether the discriminative binary code and the registered binary code are in the same class or not based on the similarity.

Preferably, in the process of constructing the binary attribute learner, the binary attributes of the target to be recognized are obtained based on the cognitive priori knowledge of the target in the sample, the sample is divided into a positive class and a negative class according to the value of each attribute, and training is carried out by using the densenet based on the collected sample and the attribute mark to obtain the binary attribute learner.

Preferably, the associated binary code learner is constructed by utilizing a convolutional neural network learning framework, a network framework adopts densenet, an associated loss function is introduced into a loss layer, as shown in formula (1), the associated binary code learner is obtained by minimizing an objective function,

where t is the number of binary attributes, Y _(i，k)For the value of the kth attribute of all samples, X is the input image, is a matrix of nxd, W _kIs the associated weight vector, and is a hyperparameter.

Preferably, in the testing stage, the image to be tested is respectively input into the binary attribute learning device and the associated binary code learning device, the discriminative binary code feature representation of the image is obtained, the discriminative binary code feature representation is compared with the registered binary code template, the similarity between the discriminative binary code feature representation and the registered binary code template is obtained by sea distance measurement, and whether the discriminative binary code and the registered binary code are in the same class or not is judged based on the similarity.

A robust binary attribute learning system, the system comprising a training module and a testing module:

the training module is used for constructing a binary attribute learning device and an associated binary code learning device in a training stage;

the test module is used for respectively inputting the image to be tested into the binary attribute learning device and the associated binary code learning device in the test stage, obtaining the discriminative binary code feature representation of the image, comparing the discriminative binary code feature representation with the registered binary code template to obtain the similarity of the discriminative binary code feature representation and the registered binary code template, and judging whether the discriminative binary code and the registered binary code are in the same class or not based on the similarity.

Preferably, in the process of constructing the binary attribute learner, the training module acquires the binary attributes of the target to be recognized based on the cognitive priori knowledge of the target in the sample, divides the sample into a positive class and a negative class according to the value of each attribute, and trains the target by using the densenet based on the collected sample and the attribute mark to obtain the binary attribute learner.

Preferably, in the training module, a convolutional neural network learning framework is used for constructing a correlated binary code learner, a network architecture adopts densenet, a correlated loss function is introduced into a loss layer, as shown in formula (1), the correlated binary code learner is obtained by minimizing an objective function,

where t is the number of binary attributes, Y _(i，k)For the value of the kth attribute of all samples, X is the input image, is a matrix of nxd, W _kIs the associated weight vector, and is a hyperparameter.

Preferably, the test module firstly inputs the image to be tested into the binary attribute learner and the associated binary code learner respectively in the test stage, discriminative binary code feature representation of the image is obtained, the discriminative binary code feature representation is compared with the registered binary code template, the similarity between the discriminative binary code feature representation and the registered binary code template is obtained by sea distance measurement, and whether the discriminative binary code and the registered binary code are in the same class or not is judged based on the similarity.

Compared with the prior art, the robust binary attribute learning method has the following outstanding beneficial effects: the robust binary attribute learning method has better comprehensibility and robustness, can hopefully improve the identification precision and efficiency, and has good popularization and application values.

Detailed Description

The robust binary attribute learning method and system of the present invention will be further described in detail with reference to the following embodiments.