Retrieval Technology Research Articles

Development and application of massive unstructured big data retrieval technology based on cloud computing platform

Development and application of massive unstructured big data retrieval technology based on cloud computing platform

Probability model of sensitive similarity measures in information retrieval

In today’s Internet age, a lot of data is stored and used, which is very important. In people’s daily life, if these data are sorted, information retrieval technology will be used, and in information retrieval, some information retrieval inaccuracies often appear. Information retrieval model is an important framework and method for fast, complete, and accurate user information retrieval. With the rapid development of information technology, great changes have taken place in people’s production and life. Various information network technologies are widely used in people’s lives. The resulting flow of information shows explosive growth, information retrieval. User requirements are getting higher and higher. How to complete personalized information retrieval in a large amount of mixed information, so that retrieval technology can help us obtain effective retrieval results, has become a realistic problem worth exploring. In this article, the application of probability model based on sensitive similarity measure in information retrieval model is analyzed, and a similarity measure algorithm based on spectral clustering is proposed. By improving the similarity measure, the sensitivity problem of scale parameters is overcome and the retrieval precision is improved. In order to better reflect the superiority of the proposed algorithm, this article compares with ng-jordan-weiss (NJW) and deep sparse subspace clustering (DSSC) algorithms. The experimental results show that the proposed algorithm is superior to NJW and DSSC algorithms for different data sets in different evaluation indicators (Rand and F-measure).

Image Retrieval Based on Deep Feature Extraction and Reduction with Improved CNN and PCA

With the rapid development of information technology, the speed and efficiency of image retrieval are increasingly required in many fields, and a compelling image retrieval method is critical for the development of information. Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete, information more complementary and higher precision. However, the high-dimension deep features extracted by CNNs (convolutional neural networks) limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval. To solving this problem, the high-dimension feature reduction technology is proposed with improved CNN and PCA quadratic dimensionality reduction. Firstly, in the last layer of the classical networks, this study makes a well-designed DR-Module (dimensionality reduction module) to compress the number of channels of the feature map as much as possible, and ensures the amount of information. Secondly, the deep features are compressed again with PCA (Principal Components Analysis), and the compression ratios of the two dimensionality reductions are reduced, respectively. Therefore, the retrieval efficiency is dramatically improved. Finally, it is proved on the Cifar100 and Caltech101 datasets that the novel method not only improves the retrieval accuracy but also enhances the retrieval efficiency. Experimental results strongly demonstrate that the proposed method performs well in small and medium-sized datasets.

Key Frame Extraction Algorithm of Motion Video Based on Priori

Key frame extraction technology is one of the core technologies of content-based video retrieval. For video types with complex content, various scenes, and rich actions, the performance of existing key frame extraction methods is not ideal. Based on the Visual Geometry Group (VGG), this article proposes an image saliency extraction model assisted by deep prior information, and uses a large-scale data set for training on the server to obtain a trained model, and then integrates multiple features. The saliency extraction algorithm is combined with the image saliency extraction model assisted by deep prior information, and a saliency extraction algorithm based on multi-feature fusion and deep prior information is proposed. A new method for extracting key frames of motion video is introduced in detail. Taking into account that sports videos in real applications are susceptible to interference from various factors, resulting in poor picture quality, this article constructs a new visual attention model for moving targets in sports videos, which integrates images. The combination of multiple features of the bottom-level features and the skin color confidence map of the moving target overcomes the problem that a single feature cannot fully express the moving target. Since the processing object in this article is for the moving target in the video of the sports room, the extracted moving target can provide samples for video post-processing. The experimental results show that the proposed key frame extraction algorithm can quickly grasp the pedestrian information in the motion video and provide effective processing samples for the motion target for video post-processing.

Application of fuzzy intelligent reasoning method in art color measurement

Under the influence of the COVID-19 epidemic situation, many countries have taken many measures to control the flow of people. The inability of people to gather makes art color measurement a problem. Color matching and coordination and color space conversion have always been the research focus of art color measurement. This paper studies a method of fuzzy intelligent reasoning in art color measurement. Based on case-based reasoning and rule-based reasoning, this method is an important self-learning and self-maintenance method in the field of artificial intelligence and expert system. On the basis of expounding the basic characteristics of color and color space, this paper designs the process principle of case-based reasoning and the process of rule-based reasoning. In this paper, case and rule knowledge representation method, case retrieval technology and rule conflict resolution strategy are established. Based on the above strategy, the color case base is established. In addition, the rule table is established by combining color-brewer with rule reasoning and referring to color-brewer. The rule table has a certain reference value for the application of intelligent reasoning method in the field of art color measurement under the influence of COVID-19 epidemic.

Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

Research on Prevention of Citrus Anthracnose Based on Image Retrieval Technology

Citrus anthracnose is a common fungal disease in citrus-growing areas in China, which causes very serious damage. At present, the manual management method is time-consuming and labor-consuming, which reduces the control effect of citrus anthracnose. Therefore, by designing and running the image retrieval system of citrus anthracnose, the automatic recognition and analysis of citrus anthracnose control were realized, and the control effect of citrus anthracnose was improved. In this paper, based on the self-collected and collated citrus anthracnose image database, we use three image features to realize an image retrieval system based on citrus anthracnose through SMV, AP clustering optimization. The results show that: 1) In the accuracy of image feature retrieval, Gist feature extraction > cumulative color histogram > Gabor texture feature; 2) In the maximum divergence diversity retrieval, semi-supervised AP clustering retrieval > AP clustering retrieval > SVM relevance feedback results > initial retrieval. 3) Practice shows that this technology can reduce the workload of monitoring and management in the control process of citrus planting area, and promote the intelligent and efficient control of citrus anthracnose, which has high practical application value.

Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology

With the rapid development of video monitoring, the massive information of the monitoring image has far exceeded the effective processing range of human resources. Intelligent video retrieval technology has become an increasingly indispensable part of video monitoring system. Intelligent video retrieval technology integrates video processing, computer vision and artificial intelligence, which greatly improves the efficiency of monitoring and the accuracy and linkage of monitoring system. Face recognition and other emerging technologies continue to rise and apply to the security monitoring system. Based on deep learning theory and face detection neural network, this paper proposes a video oriented cascaded intelligent face detection algorithm, which builds deep learning network by cascading multiple features, from edge features, contour features, local features to semantic features, and advances layer by layer. According to the last semantic features, the information of the input data is obtained to accurately realize the face detection under the non ideal condition. Simulation results show that the algorithm has good detection performance for single face and multi face images, and has strong robustness for rotating face. At the same time, the algorithm is fast and can basically meet the requirements of real-time face detection.

Research on We—Media Information Retrieval Technology of Knowledge Map

As the development direction of information retrieval technology gradually evolves toward the relationship of search entities, traditional relational databases are difficult to satisfy, and graph databases are specifically created to handle the relationships between data. This article explains the basic concept of graph database, and takes the example of domain-specific database information retrieval as an example, analyzes its advantages and disadvantages, and analyzes the challenges faced by the graph database in full-text information retrieval.

Research on Image Retrieval Algorithm Based on Combination of Color and Shape Features

With the development of content-based image retrieval technology, the retrieval efficiency of image retrieval technology is getting higher and higher. For different data images, image retrieval based on color features and shape features can be used to improve retrieval efficiency. However, when a single image feature is retrieved, its retrieval efficiency still cannot meet people’s needs. In this paper, we propose an image retrieval algorithm based on the combination of color and shape features. The cumulative histogram method is used to calculate the color features of the image, and 7 Hu invariant moments are calculated as shape features. The color and shape features are combined with certain weights, and the Euclidean distance is used as the similarity measure. Finally, the image is retrieved, and the related experiments are passed. By comparing with related experiments, the algorithm effectively improves the accuracy of image retrieval.

Application of image retrieval based on convolutional neural networks and Hu invariant moment algorithm in computer telecommunications

Image recognition and retrieval is an important application field of digital image and it has a wide range of application scenarios in the computer telecommunications. Convolutional neural networks (CNN) is a kind of feed-forward neural networks which includes convolution calculation and which has a deep structure and it is one of the typical algorithms of deep learning. In recent years, it has become a highly efficient recognition algorithm which has been widely applied in such fields as pattern recognition and image processing. Its characteristics include few training parameters and strong adaptability. On the other hand, Hu invariant moment algorithm-a conventional algorithm, is also extensively used in various image processing fields due to its simple calculation and high efficiency. This paper analyzes the research background and significance of both CNN and Hu invariant moment algorithm, and introduces their research status. Besides, it also analyzes the results of color-, distance- and weight-based Hu invariant moment algorithm, and compares it with CNN to serve as a theoretical support for better achieving image classification, recognition and retrieval technology.

Design and Implementation of Efficient Storage and Retrieval Technology of Traffic Big Data

Design and Implementation of Efficient Storage and Retrieval Technology of Traffic Big Data

A supervised term ranking model for diversity enhanced biomedical information retrieval

BackgroundThe number of biomedical research articles have increased exponentially with the advancement of biomedicine in recent years. These articles have thus brought a great difficulty in obtaining the needed information of researchers. Information retrieval technologies seek to tackle the problem. However, information needs cannot be completely satisfied by directly introducing the existing information retrieval techniques. Therefore, biomedical information retrieval not only focuses on the relevance of search results, but also aims to promote the completeness of the results, which is referred as the diversity-oriented retrieval.ResultsWe address the diversity-oriented biomedical retrieval task using a supervised term ranking model. The model is learned through a supervised query expansion process for term refinement. Based on the model, the most relevant and diversified terms are selected to enrich the original query. The expanded query is then fed into a second retrieval to improve the relevance and diversity of search results. To this end, we propose three diversity-oriented optimization strategies in our model, including the diversified term labeling strategy, the biomedical resource-based term features and a diversity-oriented group sampling learning method. Experimental results on TREC Genomics collections demonstrate the effectiveness of the proposed model in improving the relevance and the diversity of search results.ConclusionsThe proposed three strategies jointly contribute to the improvement of biomedical retrieval performance. Our model yields more relevant and diversified results than the state-of-the-art baseline models. Moreover, our method provides a general framework for improving biomedical retrieval performance, and can be used as the basis for future work.

Study on Chinese Word Segmentation

<p>Search engine technology is widely applied currently, which gradually deepens the research of full-text retrieval technology and Chinese word segmentation technology. Chinese word segmentation is one of the key technologies of Chinese languages information, the quality of which directly affects the information processing efficiency of Chinese languages. </p>

Efficient Caching for Data-Driven IoT Applications and Fast Content Delivery with Low Latency in ICN

Edge computing is a key paradigm for the various data-intensive Internet of Things (IoT) applications where caching plays a significant role at the edge of the network. This paradigm provides data-intensive services, computational activities, and application services to the proximity devices and end-users for fast content retrieval with a very low response time that fulfills the ultra-low latency goal of the 5G networks. Information-centric networking (ICN) is being acknowledged as an important technology for the fast content retrieval of multimedia content and content-based IoT applications. The main goal of ICN is to change the current location-dependent IP network architecture to location-independent and content-centric network architecture. ICN can fulfill the needs for caching to the vicinity of the edge devices without further storage deployment. In this paper, we propose an architecture for efficient caching at the edge devices for data-intensive IoT applications and a fast content access mechanism based on new clustering and caching procedures in ICN. The proposed cluster-based efficient caching mechanism provides the solution to the problem of the existing hash and on-path caching mechanisms, and the proposed content popularity mechanism increases the content availability at the proximity devices for reducing the content transfer time and packet loss ratio. We also provide the simulation results and mathematical analysis to prove that the proposed mechanism is better than other state-of-the-art caching mechanisms and the overall network efficiencies are increased.

A cross-modal multimedia retrieval method using depth correlation mining in big data environment

Cross-media retrieval is a technology aimed at breaking through the shackles of single-mode retrieval technology, which is limited to the same multimedia form. It is also hoped to be able to search each other across the media form. Comprehensive processing of different multimedia morphological data is an urgent problem to be solved in cross-media retrieval area, in other words, the semantic relationship between potential features should be mined, which will improve their similarity. To solve the above problems, a deep correlation mining method is proposed, which trains different media features by deep learning, and then fuses the correlation between the trained features to solve the heterogeneity between different features, which will make the features of different multimedia data comparable. On this basis, Levenberg-Marquart method is applied to solve the problem that deep learning is easy to fall into local minimum solution in gradient training. Experiments on different databases show that the proposed method is effective in the field of cross-media retrieval. Compared with other advanced multimedia retrieval methods, the proposed method has achieved better retrieval results.

The cloud retrieval of combining sparse representation with subspace projection

Satellite cloud imagery can show the features and the evolution processes of all kinds of cloud systems from different aspects. Thus, adopting the content-based cloud image retrieval makes a big difference in supervising present weather conditions and studying the climate change. In order to optimize the combined features of the cloud picture and strengthen the generalization ability of its combined features, this paper presents an optimal method of combining the features of the sparse representation with the subspace projection. At first, we should extract its color, texture and shape, convert all the combined features, and divide them into different blocks. Then, we can make the sparse representation for each block's features, grouping them according to different atom variance and gaining both noticeable and unnoticeable features. Finally, we can count the power of the grouped features to get the subspace projection matrix, projecting the original combined features on it and achieving the optimal cloud picture features. The experiment turns out that the method of optimizing the cloud picture features in this paper is better than common descending dimension method and cloud retrieval technology in precision ratio and recall ratio. It indeed has a stronger optimization in the combined features as well as a lower time complexity in the process of the real-time retrieval, which indicates a brand new retrieval method.

An Enhanced Framework for Content Based Medical Image Retrieval using Deep Neural Network

As the technology growth fuelled by low cost tech in the areas of compute, storage the need for faster retrieval and processing of data is becoming paramount for organizations. The medical domain predominantly for medical image processing with large size is critical for making life critical decisions. Healthcare community relies upon technologies for faster and accurate retrieval of images. Traditional, existing problem of efficient and similar medical image retrieval from huge image repository are reduced by Content Based Image Retrieval (CBIR) . The major challenging is an semantic gap in CBIR system among low and high level image features. This paper proposed, enhanced framework for content based medical image retrieval using DNN to overcome the semantic gap problem. It is outlines the steps which can be leveraged to search the historic medical image repository with the help of image features to retrieve closely relevant historic image for faster decision making from huge volume of database. The proposed system is assessed by inquisitive amount of images and the performance efficiency is calculated by precision and recall evaluation metrics. Experimental results obtained the retrieval accuracy is 79% based on precision and recall and this approach is preformed very effectively for image retrieval performance.

Reduction of urban heat island and associated greenhouse gas emissions

The reduction of urban heat island (UHI) and carbon emission is of great importance for thermal environment of urban residential areas. This paper aimed to establish a method to analyze the effect of different underlying surface layouts on the reduction of heat island intensity and carbon emission. Taking Tianjin, China, as a case study area, a computational fluid dynamics (CFD) model, satellite-based remote sensing retrieval technology, and orthogonal experimental design were performed to investigate and quantify the performance of mitigation strategies (i.e., urban green space or water body, cool/high reflectivity pavement, and reflective roofs and facades) on UHI. The results indicated that urban green space is the most natural and effective mitigation strategy, while the widespread application of cool/high reflectivity materials is suggested as a mitigation strategy for building height more than 10 m above the ground. Application of urban water body is the next preferred strategy for air at 1.5 m pedestrian level. Green roofs, rainwater gardens, and permeable brick pavements are also more feasible in high-density cities for mitigating the UHI effect. The above mitigation and adaptation strategies of community scale can be acted on UHI and as a global urban climate change response.

Frequent itemset-based feature selection and Rider Moth Search Algorithm for document clustering

Document clustering has recently been paid great attention in retrieval, navigation, and summarization of huge volumes of documents. With a better document clustering approach, computers can organize a document corpus automatically to a meaningful cluster for enabling efficient navigation, and browsing of the corpus. Document navigation and browsing is a valuable complement to the deficiencies of information retrieval technologies. This paper introduces Modsup-based frequent itemset and Rider Optimization-based Moth Search Algorithm (Rn-MSA) for clustering the documents. At first, the input documents are given to the pre-processing step, and then, the extraction is carried out based on TF-IDF and Wordnet features. Once the extraction is done, the feature selection is carried out based on frequent itemset for the establishment of feature knowledge. At last, the document clustering is done using the proposed Rn-MSA, which is designed by combining Rider Optimization Algorithm (ROA), and the Moth Search Algorithm (MSA). The performance of the document clustering based on proposed Modsup + Rn-MSA is evaluated in terms of precision, recall, F-Measure, and accuracy. The developed document clustering method achieves the maximal precision of 95.90%, maximal recall of 96.41%, maximal F-Measure of 96.41%, and the maximal accuracy of 95.12% that indicates its superiority.