# Image Network Teaching Resource Retrieval Algorithm Based on Deep Hash Algorithm

Authors: Guotao Zhao; Jie Ding
Journal: Scientific Programming
 (2021)

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## Abstract

In order to improve the retrieval ability of multiview attribute coded image network teaching resources, a retrieval algorithm of image network teaching resources based on depth hash algorithm is proposed. The pixel big data detection model of the multiview attribute coding image network teaching resources is constructed, the pixel information collected by the multiview attribute coding image network teaching resources is reconstructed, the fuzzy information feature components of the multiview attribute coding image are extracted, and the edge contour distribution image is combined. The distributed fusion result of the edge contour of the view image of the network teaching resources realizes the construction of the view feature parameter set. The gray moment invariant feature analysis method is used to realize information coding, the depth hash algorithm is used to realize the retrieval of multiview attribute coded image network teaching resources, and the information recombination is realized according to the hash coding result of multiview attribute coded image network teaching resources, thus improving the fusion. The simulation results show that this method has higher precision, better retrieval precision, and higher level of resource fusion for multiview coded image network teaching resource retrieval.


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## Body

## 1. Introduction

With the continuous enrichment of multimedia network resources, it is necessary to build a fusion model of multiview attribute coded image network teaching resources under the conditions of multimedia environment, so as to improve the sharing level and retrieval ability of network teaching resources. Due to the increasing number of multiview attribute coding image network teaching resources, it is increasingly difficult to accurately retrieve and locate the multiview attribute coding image network teaching resources. In particular, under the condition of multiview attribute coding, the environmental distribution of image network teaching resources is more disturbed, and the search accuracy and completeness rates of multiview attribute coding image network teaching resource retrieval are not high [1]. An optimized multiview attribute coding image network teaching resource retrieval model needs to be constructed to realize the retrieval and sharing of multiview attribute coding image network teaching resources by combining the distribution characteristics of multiview attribute coding image network teaching resources [2].Based on the multimedia image retrieval recognition distribution, a multiview attribute encoding image network teaching resource encoding and feature analysis model is established [3]. On the basis of feature extraction, the pixel feature analysis of multiview attribute encoded image web teaching resources is combined. The multiview attribute encoding image web-based teaching resources are realized by the semantic distribution of images. Among the traditional methods, the main methods for retrieval of multiview attribute encoded image web teaching resources are resource retrieval methods based on ontology information segmentation, multiview attribute encoded image web teaching resource retrieval methods based on Harris corner point location analysis, and multiview attribute encoded image web teaching resource retrieval methods based on evolutionary clustering analysis [4, 5]. A multiview attribute encoded image web teaching resource retrieval model based on similarity feature detection and semantic ontology fusion is proposed in [6]. He et al. constructed a parametric model for feature distribution of multiview attribute encoded image web teaching resources. It performs multiview attribute encoding image web teaching resource retrieval by fuzzy degree matching, but the accuracy of this method for multiview attribute encoding image web teaching resource retrieval is not high, and at the same time, the computational difficulty is high. An image resource retrieval method based on Harris corner point localization is proposed in [7]. The multiview attribute encoding image web-based teaching resource retrieval by corner point localization and parameter identification improves the localization ability of image retrieval, but the viewpoint discrimination of this method for image retrieval is not high.To solve the above problems, this paper proposes an image network teaching resource retrieval algorithm based on deep hashing algorithm. Firstly, a pixel big data detection model of multiview attribute encoding image network teaching resources is constructed. Then, the retrieval of multiview attribute encoded image network teaching resources is achieved by deep hash algorithm [8]. Realize information reorganization according to the result of hash coding of multiview attribute encoding image network teaching resources, and improve the fusion and retrieval ability of multimedia image network teaching resources. Finally, the performance test of image network teaching resource retrieval is carried out by simulation test and constructive conclusions are obtained.


## 2. Multiview Attribute Encoding Image Web-Based Teaching Resource Distribution and Information Preprocessing

### 2.1. Multiview Attribute Encoding Image Web-Based Teaching Resource Fusion Processing

In order to realize multiview attribute coded image web teaching resource retrieval, a feature matching model for multiview attribute coded image web teaching resource retrieval is constructed by combining semantic feature analysis and fusion scheduling methods. According to joint feature detection, association rule fusion and similarity feature detection are used for data management of multiview attribute encoded image web teaching resource retrieval [9]. The overall structure model of multiview attribute encoding image web-based teaching resource retrieval is obtained as shown in Figure 1.Figure 1
General structural model of multiview attribute encoding image web-based teaching resource retrieval.The cluster analysis method is used to obtain the cluster center of multiview attribute encoding image web teaching resource retrievalMi with Mj. Using the method of rough set feature matching, the reliability matching degree of multiview attribute encoded image web teaching resource retrieval is obtained as ClustdistMi,Mj. The edge contour feature parameter set of multiview attribute encoded image web teaching resources is extracted, and the feature fusion process of multiview attribute encoded image web teaching resources is performed in the gradient pixel space. Based on Harris corner point localization, the set of regional pixels for multiview attribute encoding feature fusion is obtained as follows:(1)R1k=R2kexp−jω0Tp/2,k=0,1,…,N−32,R2k=Akexpjφk,k=0,1,…,N−32,where R2k is the multiview attribute encoding feature component, Tp is the sampling time interval of the multiview attribute encoding image, and ω0T is the joint component. Combining the semantic information detection of network teaching resources of multiview attribute-encoded images and the analysis results of invariant moment features of multiview attribute-encoded images, the teaching resource fusion parameters are obtained as(2)φXk,ti=GXk−ti=exp−12σi2Xk−ti2=exp−12σi2∑m=1Mxkm−tim2,where ti=ti1,ti2,…,tiM is the discrete sequence to the multiview attribute encoding image web teaching resource retrieval and σi is the spatial distribution cost of multiview attribute encoding image web teaching resource retrieval.


### 2.2. Multiview Attribute Encoding Feature Analysis

The pixel information of the collected multiview attribute encoded image web-based teaching resources is structurally reorganized to extract the fuzzy information feature components of the multiview attribute encoded images [10]. The obtained distribution sequences of multiview attribute encoded image web-based teaching resources are denoted as(3)r1n=r2nexp−jω0Tp2,n=0,1,…,N−32,r2n=Aexpjω0nT+θ,n=0,1,…,N−32,where N is the sampling length of the image pixel sequence and ω0 is the grayscale distribution interval.Gradient pixel decomposition and information fusion techniques are used to construct a mean segmentation model of multiview attribute encoded image web-based teaching resources, denoted as(4)xT¯=1T∑i=1Txi,where x1,x2,x3,…,xT is the edge profile deformation parameter of the multiview attribute encoded image and T is the mean sampling interval of the multiview attribute encoded image sampling.A feature analysis model for multiview attribute encoding image web teaching resource retrieval is constructed. By feature decomposition, the RGB 3D reconstruction model of multiview attribute encoded image web teaching resource retrieval is constructed, and the RGB 3D feature detection component is obtained asNl, whose calculation formula is(5)Nl=1,l=0,L,2π⋅D/2⋅sinηltriangle,l=1,...,L−1,where ltriangle=π⋅D/2L denotes the retinex corner point parameter value of the multiview attribute encoded image web-based teaching resource and L is the gray pixel intensity.We combined the results of distributed fusion of edge contours of the view images of web teaching resources to achieve the construction of the view feature parameter set. Through the feature reconstruction and fuzzy mean clustering results [11], the comprehensive feature parameters of the web teaching resource view fusion are obtained as k=1,2,…,n,zk∈ws,ak∈1,2,…,R. The results of multiview attribute coding feature analysis are(6)σi.=μsinπe2μ,ei<μ,μ,ei≥μ,−μ,ei≤−μ,where σx,σθ, ei denote the fitness covariates for the fusion of multiview attribute encoded image web teaching resources and μ denotes the detection statistical feature quantity of each pixel point, μ>0. This enables the fusion process of multiview attribute encoding image web-based teaching resources for multiview attribute encoding feature analysis [12].


## 2.1. Multiview Attribute Encoding Image Web-Based Teaching Resource Fusion Processing

In order to realize multiview attribute coded image web teaching resource retrieval, a feature matching model for multiview attribute coded image web teaching resource retrieval is constructed by combining semantic feature analysis and fusion scheduling methods. According to joint feature detection, association rule fusion and similarity feature detection are used for data management of multiview attribute encoded image web teaching resource retrieval [9]. The overall structure model of multiview attribute encoding image web-based teaching resource retrieval is obtained as shown in Figure 1.Figure 1
General structural model of multiview attribute encoding image web-based teaching resource retrieval.The cluster analysis method is used to obtain the cluster center of multiview attribute encoding image web teaching resource retrievalMi with Mj. Using the method of rough set feature matching, the reliability matching degree of multiview attribute encoded image web teaching resource retrieval is obtained as ClustdistMi,Mj. The edge contour feature parameter set of multiview attribute encoded image web teaching resources is extracted, and the feature fusion process of multiview attribute encoded image web teaching resources is performed in the gradient pixel space. Based on Harris corner point localization, the set of regional pixels for multiview attribute encoding feature fusion is obtained as follows:(1)R1k=R2kexp−jω0Tp/2,k=0,1,…,N−32,R2k=Akexpjφk,k=0,1,…,N−32,where R2k is the multiview attribute encoding feature component, Tp is the sampling time interval of the multiview attribute encoding image, and ω0T is the joint component. Combining the semantic information detection of network teaching resources of multiview attribute-encoded images and the analysis results of invariant moment features of multiview attribute-encoded images, the teaching resource fusion parameters are obtained as(2)φXk,ti=GXk−ti=exp−12σi2Xk−ti2=exp−12σi2∑m=1Mxkm−tim2,where ti=ti1,ti2,…,tiM is the discrete sequence to the multiview attribute encoding image web teaching resource retrieval and σi is the spatial distribution cost of multiview attribute encoding image web teaching resource retrieval.


## 2.2. Multiview Attribute Encoding Feature Analysis

The pixel information of the collected multiview attribute encoded image web-based teaching resources is structurally reorganized to extract the fuzzy information feature components of the multiview attribute encoded images [10]. The obtained distribution sequences of multiview attribute encoded image web-based teaching resources are denoted as(3)r1n=r2nexp−jω0Tp2,n=0,1,…,N−32,r2n=Aexpjω0nT+θ,n=0,1,…,N−32,where N is the sampling length of the image pixel sequence and ω0 is the grayscale distribution interval.Gradient pixel decomposition and information fusion techniques are used to construct a mean segmentation model of multiview attribute encoded image web-based teaching resources, denoted as(4)xT¯=1T∑i=1Txi,where x1,x2,x3,…,xT is the edge profile deformation parameter of the multiview attribute encoded image and T is the mean sampling interval of the multiview attribute encoded image sampling.A feature analysis model for multiview attribute encoding image web teaching resource retrieval is constructed. By feature decomposition, the RGB 3D reconstruction model of multiview attribute encoded image web teaching resource retrieval is constructed, and the RGB 3D feature detection component is obtained asNl, whose calculation formula is(5)Nl=1,l=0,L,2π⋅D/2⋅sinηltriangle,l=1,...,L−1,where ltriangle=π⋅D/2L denotes the retinex corner point parameter value of the multiview attribute encoded image web-based teaching resource and L is the gray pixel intensity.We combined the results of distributed fusion of edge contours of the view images of web teaching resources to achieve the construction of the view feature parameter set. Through the feature reconstruction and fuzzy mean clustering results [11], the comprehensive feature parameters of the web teaching resource view fusion are obtained as k=1,2,…,n,zk∈ws,ak∈1,2,…,R. The results of multiview attribute coding feature analysis are(6)σi.=μsinπe2μ,ei<μ,μ,ei≥μ,−μ,ei≤−μ,where σx,σθ, ei denote the fitness covariates for the fusion of multiview attribute encoded image web teaching resources and μ denotes the detection statistical feature quantity of each pixel point, μ>0. This enables the fusion process of multiview attribute encoding image web-based teaching resources for multiview attribute encoding feature analysis [12].


## 3. Image Network Teaching Resource Retrieval Algorithm

### 3.1. Multiview Attribute Encoding Image Depth Hash Encoding

Letyn be the amount of rough set features for multiview attribute encoded image web teaching resource retrieval, and according to the rough set feature matching results, the depth hash encoding algorithm is used to perform depth feature detection for multiview attribute encoded image web teaching resources [13]. The gradient value of each multiview attribute encoded image network teaching resource is calculated, and the depth hash coding convergence value is obtained as(7)PlimT⟶∞xT¯=K=1,where xT¯ denotes the correlation parameter between feature sets, K is the scale of feature vector encoding, and Qxi,yi is the color moment of the training image and is the fusion result based on semantic information. When i≠j,1≤i≤q,1≤j≤q, the joint feature generalization of the output of the multiview attribute encoded image web-based teaching resource retrieval is obtained as(8)Ft=Xpu−vsina=3N+12xN+1x3N+1−τ,where Xp is the source information of the semantic distribution of the multiview attribute encoded image web teaching resources, u is the joint distribution feature quantity of the multiview attribute encoded image web teaching resources, and v is the normative fuzzy detection basis function of the multiview attribute encoded image web teaching resources. Set i=1, the first multiview attribute coded image [14], and combined with the distribution of network teaching resources, the output of the depth hash code of the multiview attribute coded image network teaching resource is(9)pthbint=Ct∑xi∈wkxiδhxi−bint,ptebine=Ce∑xi∈wkxi2hisxiδvxi−bine,where Ct=Ce=1/∑xi∈wkxi2 denotes the depth information parameter of the multiview attribute encoding image web teaching resource aggregation and bine∈1,M denotes the attribute category. In summary, the analysis is carried out to achieve deep hash encoding of multiview attribute encoded image network teaching resources [15].


### 3.2. Image Network Teaching Resource Retrieval Output

In order to obtain feature reconstruction and fuzzy mean clustering results, a gray invariant moment feature analysis method is used to achieve information encoding. A deep hashing algorithm is used to implement the retrieval of teaching resources for multiview attribute encoding image networks, and according to the depth feature weighting training [16], the weighted aggregation output function of teaching resources for multiview attribute encoding image networks is obtained and expressed as(10)ESnake=∑0N−1Eintvi+Eextvi,where Vi is the morphological function for the weighted aggregation of multiview attribute encoded image web-based teaching resources and i=0,1,…,N−1 is the set of pixel points. The multiview attribute encoding aggregation output is G.(11)Gt=AFt−1+t.Ft=xt,ytT is the associated pixel value of the tth frame of the multiview attribute encoded image teaching resource. The semantic association distribution model of multiview attribute encoded image web-based teaching resource retrieval is constructed to obtain the output retrieval trajectory distribution of(12)tracex,y,σn>tracex,y,σ1,l∈n−1,n+1,where trace. denotes the fusion parameter of multiview attribute encoding image web teaching resource retrieval. The resulting iterative function for multiview attribute encoding image web-based teaching resource retrieval is(13)Xt=AXt−1+t,where X=xt,ytT is the training image set, and the output clustering matrix of the multiview attribute encoded image network teaching resource retrieval is obtained as(14)H=Lxxx,σLxyx,σLxyx,σLyyx,σ,where Lxxx,σ is the feature matching coefficient of multiview attribute encoded image web teaching resource retrieval and Lyy and Lxy are the pixel components of multiview attribute encoded image web teaching resource retrieval in different aggregation directions, respectively.In summary, according to the analysis, the hash coding results of multiview attribute encoding image network teaching resources are realized to reorganize information and improve the multimedia image network teaching resource fusion and retrieval capability. The structure diagram of the implementation of the improved algorithm is shown in Figure2.Figure 2
Algorithm optimization implementation flow.


## 3.1. Multiview Attribute Encoding Image Depth Hash Encoding

Letyn be the amount of rough set features for multiview attribute encoded image web teaching resource retrieval, and according to the rough set feature matching results, the depth hash encoding algorithm is used to perform depth feature detection for multiview attribute encoded image web teaching resources [13]. The gradient value of each multiview attribute encoded image network teaching resource is calculated, and the depth hash coding convergence value is obtained as(7)PlimT⟶∞xT¯=K=1,where xT¯ denotes the correlation parameter between feature sets, K is the scale of feature vector encoding, and Qxi,yi is the color moment of the training image and is the fusion result based on semantic information. When i≠j,1≤i≤q,1≤j≤q, the joint feature generalization of the output of the multiview attribute encoded image web-based teaching resource retrieval is obtained as(8)Ft=Xpu−vsina=3N+12xN+1x3N+1−τ,where Xp is the source information of the semantic distribution of the multiview attribute encoded image web teaching resources, u is the joint distribution feature quantity of the multiview attribute encoded image web teaching resources, and v is the normative fuzzy detection basis function of the multiview attribute encoded image web teaching resources. Set i=1, the first multiview attribute coded image [14], and combined with the distribution of network teaching resources, the output of the depth hash code of the multiview attribute coded image network teaching resource is(9)pthbint=Ct∑xi∈wkxiδhxi−bint,ptebine=Ce∑xi∈wkxi2hisxiδvxi−bine,where Ct=Ce=1/∑xi∈wkxi2 denotes the depth information parameter of the multiview attribute encoding image web teaching resource aggregation and bine∈1,M denotes the attribute category. In summary, the analysis is carried out to achieve deep hash encoding of multiview attribute encoded image network teaching resources [15].


## 3.2. Image Network Teaching Resource Retrieval Output

In order to obtain feature reconstruction and fuzzy mean clustering results, a gray invariant moment feature analysis method is used to achieve information encoding. A deep hashing algorithm is used to implement the retrieval of teaching resources for multiview attribute encoding image networks, and according to the depth feature weighting training [16], the weighted aggregation output function of teaching resources for multiview attribute encoding image networks is obtained and expressed as(10)ESnake=∑0N−1Eintvi+Eextvi,where Vi is the morphological function for the weighted aggregation of multiview attribute encoded image web-based teaching resources and i=0,1,…,N−1 is the set of pixel points. The multiview attribute encoding aggregation output is G.(11)Gt=AFt−1+t.Ft=xt,ytT is the associated pixel value of the tth frame of the multiview attribute encoded image teaching resource. The semantic association distribution model of multiview attribute encoded image web-based teaching resource retrieval is constructed to obtain the output retrieval trajectory distribution of(12)tracex,y,σn>tracex,y,σ1,l∈n−1,n+1,where trace. denotes the fusion parameter of multiview attribute encoding image web teaching resource retrieval. The resulting iterative function for multiview attribute encoding image web-based teaching resource retrieval is(13)Xt=AXt−1+t,where X=xt,ytT is the training image set, and the output clustering matrix of the multiview attribute encoded image network teaching resource retrieval is obtained as(14)H=Lxxx,σLxyx,σLxyx,σLyyx,σ,where Lxxx,σ is the feature matching coefficient of multiview attribute encoded image web teaching resource retrieval and Lyy and Lxy are the pixel components of multiview attribute encoded image web teaching resource retrieval in different aggregation directions, respectively.In summary, according to the analysis, the hash coding results of multiview attribute encoding image network teaching resources are realized to reorganize information and improve the multimedia image network teaching resource fusion and retrieval capability. The structure diagram of the implementation of the improved algorithm is shown in Figure2.Figure 2
Algorithm optimization implementation flow.


## 4. Simulation Experiments and Result Analysis

In order to verify the application performance of this paper’s method in implementing multiview attribute coding image network teaching resource retrieval, Matlab is used for simulation test analysis. The number of training samples for multiview attribute coding image network teaching resources is set to 4000, the test set is 490, the number of training iteration steps for deep hash coding is 240, the scale is 0.32, and other parameters are set as shown in Table1.Table 1
Image network teaching resource retrieval parameters.
Image samplesPixelScale factorSimilarityRose520.8000.2270.407Plane549.3600.2210.396Bridge514.0800.2180.391Building559.4400.2230.400Elephant500.6400.2310.415According to the above parameter settings, multiview attribute encoded image web-based teaching resource retrieval is performed. Two sets of samples are taken, and the multiview attribute encoding image samples are obtained as shown in Figure3.Figure 3
Sample multiview attribute encoded images. (a) Building. (b) Elephant.
(a)(b)The image of Figure3 is used as the sample object to implement image depth hash coding, and the image hash coding fusion results are obtained as shown in Figures 4 and 5.Figure 4
Building image retrieval results. (a) Integration. (b) Deep hash encoding.
(a)(b)Figure 5
Elephant image retrieval results. (a) Integration. (b) Deep hash encoding.
(a)(b)Analysis of Figures4 and 5 shows that the method of this paper can effectively achieve the retrieval of multiview attribute encoded image web teaching resources. The performance of the retrieval output is good. The performance of multiview attribute coded image web teaching resource retrieval was tested, and the comparison results in terms of retrieval accuracy are shown in Table 2, and the analysis of the results in Table 2 shows that the retrieval accuracy of multiview attribute coded image web teaching resource retrieval by this method is high.Table 2
Comparison of image web-based teaching resource retrieval performance.
Number of iterationsMethodology of this articleWavelet transformSpectrum detection100.8600.7650.561200.8720.7990.594300.8780.7810.577400.8810.7590.555500.8860.7680.564600.8870.7960.592700.8950.7670.564800.8980.7920.588900.9020.7840.5801000.9050.7760.5721100.9060.7470.5441200.9100.7770.5731300.9240.7920.5881400.9270.7570.5531500.9330.7630.559


## 5. Conclusion

An optimized multiview attribute encoding image network teaching resource retrieval model is constructed. Combined with the distribution characteristics of multiview attribute encoding image network teaching resources, the retrieval and sharing of multiview attribute encoding image network teaching resources are realized. This paper proposes the image network teaching resource retrieval algorithm based on deep hashing algorithm. The feature matching model for multiview attribute encoding image network teaching resource retrieval is constructed. Deep feature detection is performed for multiview attribute encoding image network teaching resources. The information reorganization is realized according to the hash coding results of multiview attribute encoded image network teaching resources, and the multimedia image network teaching resource fusion and retrieval capability is improved. The gradient value of each multiview attribute encoded image web teaching resource pixel is calculated. The deep hash encoding algorithm is used to realize the multiview attribute encoding image web teaching resource retrieval. It is learned that the method in this paper has a high rate of completeness and accuracy for multiview attribute encoding image web-based teaching resource retrieval.



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*Source: 9683908-2021-10-11.xml*