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

Xiong, Zenggang; Zhi-wen, Tang; Chen, Xiaowen; Zhang, Xuemin; Kaibin, Zhang; Ye, Conghuan

doi:10.1007/s11265-019-01508-y

Cited by 95 publications

(41 citation statements)

References 18 publications

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“…Using business ecosystems in describing the cultural industry, Liu argues that there are adaptive, evolutionary, networked, and self-organizing characteristics of business ecosystems [17]. In her study of new media ecology, Zeng gang argues that the characteristics of new media ecology are digital, interactive, hypertextual, networked, and virtual reality [18].…”

Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Information Fusion Algorithm for Big Data in Digital Publishing Industry Chain

He¹

2021

Complexity

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This paper studies the information of big data in the digital publishing industry chain and adopts advanced algorithms for its fusion calculation. The basic theory of digital publishing ecological chain is dissected, the construction requirements, construction methods, and construction paths of digital publishing ecological chain are analysed, and feasible construction measures are proposed. It also defines the connotation of the fusion of knowledge services between publishing institutions and libraries in the digital era; then analyses the characteristics and principles of the fusion of knowledge services between publishing institutions and libraries in the digital era; and finally sorts out the theoretical foundations such as synergy theory, information integration service theory, and game theory. Meanwhile, this paper also studies the flow of digital publishing resources and empirically analyses the eco-efficiency of digital publishing eco-chain by using metabolic network analysis, population analysis, and life cycle analysis, and it finds the eco-efficiency problems of the existing e-book eco-chain from its analysis. Finally, the imbalance in the digital publishing ecological chain and its hazards are analysed, and specific regulation and optimization measures are proposed. The research in this paper makes up for the deficiencies of related studies and can well solve the problems that exist in the previous and provide theoretical support for the healthy development of digital publishing enterprises.

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Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Information Fusion Algorithm for Big Data in Digital Publishing Industry Chain

He¹

2021

Complexity

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“…The more common features are color features and texture features [11,12]. For example, the feature matching method based on color histogram proposed by Ballard and Seain [13]. Due to the single use of global features, the background recognition effect is very poor.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Content-Based E-Commerce Image Classification Research

Zhang

2020

IEEE Access

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The 21st century is the era of big data in the Internet. Online shopping has become a trend, and e-commerce has developed rapidly. With the exponential increase of the amount of commodity image data, the management of massive commodity image database restricts the development of e-commerce to some extent. In order to effectively manage goods and improve the accuracy and efficiency of product image retrieval, this paper uses content-based methods to classify e-commerce images. Aiming at the problems of insufficient classification accuracy and long classification training time in e-commerce image classification, an adaptive momentum learning rate based LBP-DBN training algorithm-AML-LBP-DBN and commodity image classification method based on image local feature multi-level clustering and image-class nearest neighbor classifier are proposed. By simulating the commodity identification dataset RPC, the results show that the proposed method has obvious advantages in the classification training time and classification accuracy of e-commerce images.

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“…Based on the above model, this article also refers to the idea of pretraining and adopts the method of model two-stage training [31][32][33][34].…”

Section: Model Parameter Designmentioning

confidence: 99%

Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

2021

Complexity

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This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.

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

Cited by 95 publications

References 18 publications

[Retracted] Information Fusion Algorithm for Big Data in Digital Publishing Industry Chain

[Retracted] Information Fusion Algorithm for Big Data in Digital Publishing Industry Chain

Retracted: Content-Based E-Commerce Image Classification Research

Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

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