CN110941734B - Depth unsupervised image retrieval method based on sparse graph structure - Google Patents
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Abstract
The invention provides a depth unsupervised image retrieval method based on a sparse graph structure, which comprises the steps of preprocessing a training data set and extracting image features of the training data set; constructing a weighted sparse graph, and determining a network model according to the sparse graph; training the network model by using the image characteristics of the training data set and the weighted sparse graph; extracting image features of an image to be detected, inputting the image features of the image to be detected into a network model, extracting the output of an encoder network as low-dimensional features of the image to be detected, and performing binary quantization on the low-dimensional features to obtain a hash code of the image to be detected; and calculating the Hamming distance between the image to be detected and all the image hash codes in the database to be inquired, and obtaining an approximate image according to the calculation result. The invention adopts the sparse graph structure to store the similarity information, saves the storage space of the graph structure and can avoid the requirement of retrieval performance on the number of categories.
Description
Technical Field
The invention belongs to the computer vision and pattern recognition technology, and particularly relates to a depth unsupervised image retrieval method based on a sparse graph structure.
Background
In recent years, research on image retrieval direction has been greatly improved, but today, methods for achieving excellent effects require class labels, and manual labeling is time-consuming and not professional enough for large-scale data sets in real life, so that an unsupervised image retrieval algorithm is produced and is increasingly popular. Unsupervised algorithms can avoid the need for tags, but inevitably cause a decrease in retrieval performance.
Currently, for unsupervised image retrieval, there are three main categories, namely, graph structure-based retrieval, pseudo tag-based retrieval and depth unsupervised retrieval. The image retrieval method based on the graph structure creates the graph structure to store the neighbor information of the original space before training, and then uses the neighbor relation in the graph structure for training, so that the generated hash code contains the neighbor information of the original space. The traditional graph structure comprises a Laplace graph and an anchor graph, and the two graphs have the defects of storing excessive information, being easily interfered by redundant information and having certain requirements on storage space. The method based on the pseudo label can solve the two defects, the training process is supervised mainly through the manually labeled information, however, the image retrieval capability still needs to be improved due to the influence of the defects of the manual label, and even some traditional pseudo label algorithms are very sensitive to the preset category number.
Disclosure of Invention
The invention aims to provide a depth unsupervised image retrieval method based on a sparse graph structure.
The technical solution for realizing the purpose of the invention is as follows: a depth unsupervised image retrieval method based on a sparse graph structure comprises the following specific steps:
step 1, preprocessing a training data set, and extracting image features of the training data set;
step 2, constructing a weighted sparse graph, and determining a network model according to the sparse graph, wherein the network model uses a symmetrical self-encoder structure and comprises an encoder and a decoder;
step 3, training the network model by using the image characteristics of the training data set and the weighted sparse graph;
step 4, extracting image features of the image to be detected according to the step 1, taking the image features of the image to be detected as input of a network model, extracting output of an encoder network as low-dimensional features of the image to be detected, and performing binary quantization on the low-dimensional features to obtain a hash code of the image to be detected;
and 5, calculating the Hamming distance between the image to be detected and all image hash codes in the database to be inquired, judging whether the distance value is smaller than a preset threshold value or sequencing all the distance values, and outputting the corresponding image in the database to be inquired as an approximate image of the image to be detected according to a comparison result or a sequencing result.
Preferably, the specific method for preprocessing the training data set is as follows:
the fc7 layer 4096 dimensional image features were extracted by inputting the training dataset images into a VGG16 network pre-trained by classification on the ImageNet dataset.
Preferably, the specific steps of constructing the weighted sparse graph and determining the network model according to the sparse graph are as follows:
step 2-1, taking the image characteristics of the training data set as samples, and calculating the similarity among all the samples;
step 2-2, respectively sequencing the similarity values of each sample and other samples from high to low;
2-3, respectively selecting the first k adjacent samples for each sample, and connecting to form a sparse graph;
2-4, calculating the weight of each edge according to the degree of the node pair connected with the edge in the sparse graph to form a weighted sparse graph;
2-5, determining a network model according to the weighted sparse graph, wherein the network model uses a symmetrical self-encoder structure, and a training formula of the self-encoder network is as follows:
wherein omega B Representing a set of edges in the sparse graph structure of the present invention, each edge being represented by a connected pair of nodes, w ij Represents the weight of the edge (i, j), β represents the relative contribution of the third term to the overall formula, z n Representing the output of the encoder, i.e. z n ＝f(x n Θ), Θ denotes the encoder network weight parameter, u n Representing the decoder output, i.e. u n ＝g(x n And theta, lambda) represents the decoder network weights. b is a mixture of n From z n Binary quantized, alpha denotes the relative importance of the second term, the index n denotes the nth image,
feature (N is 1,2, … N), z representing the nth image i -z j 。
Preferably, the similarity between samples is calculated by the following formula:
wherein x i And x j Respectively representing the image characteristics of two images, subscripts i and j representing the serial numbers of the images, | · | survival 2 Representing the vector two norm, s ij Indicating the similarity between the two.
Preferably, the weight of each edge is calculated in the following manner:
wherein, d m Degree of m-th side, w ij And representing the weight of an edge connecting the ith sample and the jth sample in the weighted sparse graph.
Preferably, the specific method for training the network model by using the image features of the training data set and the weighted sparse graph is as follows:
step 3-1, according to the training formula of the self-encoder network and the output z of the encoder n Updating encoder weights for gradients of the encoder weights, wherein:
training formula pair z from the encoder network n The gradient of (d) is:
according to the chain rule, the updating process of Θ is:
eta is the learning rate;
step 3-2, updating the decoder network weight parameter Lambda by using a gradient descent mode, wherein the updating formula is as follows:
3-3, updating the corresponding binary hash code, wherein an updating formula is as follows:
b n new ＝sign(z n new )＝sign(f(x n ,Θ new ))
and 3-4, repeating the steps 3-1 to 3-3 until the difference value of the training formulas of the self-encoder network in the last two times is smaller than a preset value.
Compared with the prior art, the invention has the remarkable advantages that:
(1) the invention adopts the sparse graph structure to store the similarity information, saves the storage space of the graph structure and can avoid the requirement of retrieval performance on the number of categories;
(2) according to the invention, the sparse graph is added in the field of image retrieval for the first time to store effective neighbor information, the sparse graph structure is more sparse than the traditional Laplace graph and anchor graph, and the required storage space is smaller, so that the method can be used for retrieving a larger-scale image set;
(3) the sparse graph structure can store sufficient and effective information, so that the dependence on the number of categories is reduced; on the other hand, because the redundant edges are deleted from the sparse graph, the interference of redundant information on the final performance can be avoided;
(4) the method has simple algorithm, although a network structure is used, the number of network layers is less, the requirement on the performance of a machine display card is lower, the running speed is high, and the process of creating the sparse graph can be completed by using a CPU and a machine memory, so that the method can be widely applied to occasions with larger sample size.
The present invention is described in further detail below with reference to the attached drawings.
Drawings
FIG. 1 is a flow chart of a sparse graph structure-based deep unsupervised image retrieval method during training.
Fig. 2 is a flowchart of a depth unsupervised image retrieval method based on a sparse graph structure during testing.
Detailed Description
A depth unsupervised image retrieval method based on a sparse graph structure comprises the following specific steps:
step 1, preprocessing a training data set, and extracting image characteristics of the training data set;
the training data set comprises N images, the N images of the training data set are input into a VGG16 network which is subjected to classification pre-training on the ImageNet data set to extract fc7 layers of 4096-dimensional image features, and the image features are combined into a matrix X, wherein the matrix X comprises the image features
(where d is 4096),
the feature of the nth image (N is 1,2, … N) is shown. The class labels of the training set are not known.
Step 2, constructing a weighted sparse graph, and determining a network model according to the sparse graph, wherein the specific steps are as follows:
step 2-1, taking the image characteristics of the training data set as samples, and calculating the similarity among all samples, wherein the similarity can be calculated in a cosine similarity mode, and the calculation formula is as follows:
wherein x i And x j Respectively representing the image characteristics of the two images, the indices i and j representing the image numbers, · 2 Representing the vector two norm, s ij Indicating the similarity between the two.
Step 2-2, respectively sequencing the similarity values of each sample and other samples from high to low;
2-3, respectively selecting front k adjacent samples for each sample, and connecting to form a sparse graph;
step 2-4, calculating the weight of each edge according to the degree of the node pair connected with the edge in the sparse graph to form a weighted sparse graph, wherein the weight calculation mode is as follows:
wherein d is m Degree of m-th side, w ij And representing the weight of an edge connecting the ith sample and the jth sample in the weighted sparse graph.
2-5, determining a network model according to the weighted sparse graph, wherein the network model uses a symmetrical self-encoder structure (comprising an encoder and a decoder) to carry out low-dimensional feature mapping, the encoder and the decoder both adopt three layers of fully-connected networks, the output dimension of the encoder is 1000, 2000 and L in sequence, wherein L represents the length of the encoder outputting binary codes; the decoder network output dimension is 2000, 1000 and 4096 in sequence.
After adding the weighted sparse graph structure, the training formula of the self-encoder network is as follows:
wherein omega B Representing a set of edges in the sparse graph structure of the present invention, each edge being represented by a connected pair of nodes, e.g. (i, j), w ij Represents the weight of the edge (i, j), β represents the relative contribution of the third term to the overall formula, z n Representing the output of the encoder, u n Representing the decoder output, b n From z n Quantized binary, α represents the relative importance of the second term, and the index n represents the nth image.
Step 3, training the network model by using the image characteristics of the training data set and the weighted sparse graph;
the model of the invention needs to train the weight parameters of the encoder and decoder networks, which are respectively expressed by theta and lambda, and z is recorded n ＝f(x n Theta) and u n ＝g(x n Θ, Λ) (N is 1,2, …, N) is the output of the encoder and decoder respectively, with the nth image feature as input, and b is written n And the binary hash code is the binary hash code corresponding to the nth image. Various optimization methods can be used in the deep neural network, and a gradient method is taken as an example. The specific updating steps are as follows:
step 3-1, according to the training formula of the self-encoder network and the output z of the encoder n The encoder weights are updated for the gradient of the encoder weights. Wherein the training formula (3) is to z n The gradient of (d) can be written as:
then according to the chain rule, the update procedure of Θ is:
thus, the weight parameters of the network part of the encoder can be updated in a back propagation mode, wherein eta is called a learning rate, and the main control parameter is updated in a single step.
Step 3-2, updating the decoder network weight parameter Λ in a gradient descending manner, wherein the updating formula is as follows:
it is assumed here that the learning rates of the encoder and decoder are the same.
And 3-3, updating the corresponding binary hash code, wherein an updating formula is as follows:
b n new ＝sign(z n new )＝sign(f(x n ,Θ new )) (7)
and 3-4, returning to the step 3-1, and repeating the steps 3-1 to 3-3 until the difference value of the training formulas (3) in the last two times is smaller than a preset value.
Step 4, extracting the image characteristics of the image to be detected according to the step 1, and enabling the image characteristics x of the image to be detected c As the input of the network model, extracting the output of the encoder network as the low-dimensional characteristic of the image to be tested, wherein the dimension of the low-dimensional characteristic is the same as the length of the required binary Hash code, and performing binary quantization on the low-dimensional characteristic by using a sign function to obtain the Hash code b of the image to be tested c I.e. b c ＝sign(z c ) Wherein z is c ＝f(x c Theta) as input image feature x to be measured c The encoder output of the time. When inquiring, the image to be detected is taken as the inquiry input, the Hamming distance between the image to be detected and all image Hash codes in the database to be inquired is calculated, whether the distance value is smaller than the preset threshold value or all the distance values are sequenced is judged, and the comparison result or the sequencing result is output to the database to be inquiredThe corresponding image is used as an approximate image of the image to be measured. In the testing stage, the image to be tested is the test set image.
As shown in fig. 1, images of a training set are classified and pre-trained on an ImageNet data set, and output fc 7-layer high-dimensional features through a VGG16 network, sparse graphs are constructed for the high-dimensional features by using a kNN algorithm, weights of edges are calculated according to the degree of each edge node pair in the graphs, and a weighted sparse graph is formed, and the sparse graph structure can enable hash codes generated by training to keep original spatial information as much as possible. The image high-dimensional features are input into a self-encoder network, the output of an encoder is subjected to binary quantization to be used as a hash code generated in a training process, and the output of a decoder can be used as a reconstruction feature, so that the encoder output stores more information of the original high-dimensional features. The formula in the whole training process is divided into three parts, namely quantization loss, reconstruction loss and neighbor keeping information, wherein the quantization loss and the neighbor keeping loss are used for training an encoder network during training, and the reconstruction loss is used for training a decoder network.
As shown in fig. 2, in the retrieval process, the high-dimensional features of the image to be detected are input into the trained encoder network, and binary quantization is performed on the output of the encoder network to obtain the hash code corresponding to the input image. Calculating the Hamming distance between the image to be detected and the image Hash codes in the database to be inquired, judging whether the distance value is smaller than a preset threshold value or sequencing the distance values of all the images to be detected, and outputting the corresponding image as an approximate image of the image to be detected according to a comparison result or a sequencing result.
Compared with the traditional graph, the sparse graph structure used by the invention is more sparse, so that the interference of redundant information and the requirement on storage space can be reduced; meanwhile, the information stored in the graph structure is richer than the artificially labeled pseudo label information, so that the retrieval performance can be further improved on the basis of the traditional pseudo label algorithm. In addition, the utilization of the deep network structure can further improve the learning capability of the invention on the hash code, thereby increasing the practicability of the invention.
Claims (4)
1. A depth unsupervised image retrieval method based on a sparse graph structure is characterized by comprising the following specific steps:
step 1, preprocessing a training data set, and extracting image characteristics of the training data set;
step 2, constructing a weighted sparse graph, and determining a network model according to the sparse graph, wherein the network model uses a symmetrical self-encoder structure and comprises an encoder and a decoder, and the specific steps are as follows:
step 2-1, taking the image characteristics of the training data set as samples, and calculating the similarity among all the samples;
step 2-2, respectively sequencing the similarity values of each sample and other samples from high to low;
2-3, respectively selecting the first k adjacent samples for each sample, and connecting to form a sparse graph;
2-4, calculating the weight of each edge according to the degree of the node pair connected with the edge in the sparse graph to form a weighted sparse graph;
2-5, determining a network model according to the weighted sparse graph, wherein the network model uses a symmetrical self-encoder structure, and a training formula of the self-encoder network is as follows:
wherein omega B Representing a set of edges in a sparse graph structure, each edge being represented by a connected pair of nodes, w ij Represents the weight of the edge (i, j), β represents the relative contribution of the third term to the overall formula, z n Representing the output of the encoder, i.e. z n ＝f(x n Θ), Θ denotes the encoder network weight parameter, u n Representing the decoder output, i.e. u n ＝g(x n Θ, Λ), Λ represents the decoder network weights, b n From z n Binary quantized, alpha denotes the relative importance of the second term, the index n denotes the nth image,
to representFeatures of the nth image (N ═ 1,2, … N);
step 3, training the network model by using the image characteristics of the training data set and the weighted sparse graph, wherein the specific method comprises the following steps:
step 3-1, according to the training formula of the self-encoder network and the output z of the encoder n Updating encoder weights for gradients of the encoder weights, wherein:
training formula pair z from the encoder network n The gradient of (d) is:
according to the chain rule, the update process of Θ is:
eta is the learning rate;
step 3-2, updating the decoder network weight parameter Lambda by using a gradient descent mode, wherein the updating formula is as follows:
3-3, updating the corresponding binary hash code, wherein an updating formula is as follows:
b n new ＝sign(z n new )＝sign(f(x n ,Θ new ))
3-4, repeating the steps 3-1 to 3-3 until the difference value of the training formulas of the self-encoder network in the last two times is smaller than a preset value;
step 4, extracting image features of the image to be detected according to the step 1, taking the image features of the image to be detected as input of a network model, extracting output of an encoder network as low-dimensional features of the image to be detected, and performing binary quantization on the low-dimensional features to obtain a hash code of the image to be detected;
and 5, calculating the Hamming distance between the image to be detected and all image hash codes in the database to be inquired, judging whether the distance value is smaller than a preset threshold value or sequencing all the distance values, and outputting the corresponding image in the database to be inquired as an approximate image of the image to be detected according to a comparison result or a sequencing result.
2. The method for deep unsupervised image retrieval based on sparse graph structure as claimed in claim 1, wherein the specific method for preprocessing the training data set is as follows:
the fc7 layer 4096 dimensional image features were extracted by inputting the training dataset images into a VGG16 network pre-trained by classification on the ImageNet dataset.
3. The method for retrieving the depth unsupervised image based on the sparse graph structure as claimed in claim 1, wherein the similarity calculation formula among the samples is as follows:
wherein x is i And x j Respectively representing the image characteristics of two images, subscripts i and j represent the serial numbers of the images, | 2 Representing the vector two norm, s ij Indicating the similarity of the two.
4. The sparse graph structure-based depth unsupervised image retrieval method according to claim 1, wherein the weight of each edge is calculated in a manner that:
wherein d is m Degree of m-th side, w ij And representing the weight of an edge connecting the ith sample and the jth sample in the weighted sparse graph.
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