Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

Kishore, D. Ratna; Rao, Chanamallu Srinivasa

doi:10.18280/mmep.080114

Cited by 3 publications

(1 citation statement)

References 26 publications

(29 reference statements)

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“…The frequency method transforms the gray distribution into the frequency distribution to describe the texture features, like the Fourier transform and wavelet transform. 25 The model method uses a small number of parameters to establish a mathematical model to describe texture features, like the fractal model. 26 The structure method assumes that the texture pattern is composed of a fixed, regular, and repeated primitive and quantitatively analyzes the distribution characteristics 27 or arrangement rules 28 to represent the texture features.…”

Section: Feature Extraction Of Fabric Imagementioning

confidence: 99%

Research progress of content-based fabric image retrieval

Zhang

Xiang

Wang

et al. 2022

Textile Research Journal

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The application of content-based image retrieval method aims at retrieving similar fabric images and obtaining the existing process parameters to guide production. The process of sample analysis, trial weaving, and proofing can be eliminated in sample imitation production to give full play to the advantages of historical production experience and improve the core competitiveness of enterprises. By investigating and analyzing the applications of content-based image retrieval method technology in fabric retrieval, this article provides a detailed classification and summary of the existing fabric retrieval methods using content-based image retrieval method from six aspects: image preprocessing, feature extraction, similarity measurement, retrieval strategy, dataset construction, and evaluation metrics in the common framework of content-based image retrieval method. The advantages and disadvantages of different methods are analyzed and compared. Finally, the urgent problems and future research directions of fabric image retrieval are discussed, providing ideas for scholars to further study the retrieval methods. Taking fabric as the medium, this article combs the industrial application research and development process of content-based image retrieval method technology, which is helpful to understand the application examples of computer technology and provide research ideas for the application of different computer technologies in the textile industry.

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Section: Feature Extraction Of Fabric Imagementioning

confidence: 99%

Research progress of content-based fabric image retrieval

Zhang

Xiang

Wang

et al. 2022

Textile Research Journal

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An efficient network based on double constrained loss for fabric image retrieval

Gui,

Wu,

2023

SIViP

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In order to efficiently retrieve the same or similar fabric samples, a fabric image retrieval model based on deep hashing is proposed. The model is improved on the basis of MobileNetV1, and the performance of the model is improved by combining the h-swish activation function and attention mechanism module. The hashing method is used to solve the problem of low feature matching speed caused by high dimensional feature output. By combining the label information and similarity information of the images, a new loss function is constructed, which solves the problem of low model accuracy caused by the large feature difference between similar samples and the small feature difference between heterogeneous samples in fabric images. The experimental results on self-built fabric image dataset showed that the feature extraction time of the proposed algorithm was 0.25ms, and the MAP reached 93.2%, which can take into account the fabric retrieval speed and improve retrieval accuracy at the same time, and has certain application prospects.

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Formation of a generalized semantic network of concepts

Onyshchenko¹,

Korolova²,

Nosyk³

2021

A.I.S.

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The subject of research is the features of formation of a generalized semantic network of concepts. The purpose of article is to substantiate the composition and main types of nodes and relationships characteristic of the semantic network of concepts, as well as the formation of a generalized semantic network of concepts for structural and linguistic recognition of images in computer systems and networks. Research methods: methods of the theory of mathematical logic, mathematical linguistics and set theory; methods of information visualization using graphs. Results: an approach to the formation of a generalized semantic network of structural and linguistic concepts of contour images of objects obtained at different shooting angles is proposed; the substantiation of the composition and types of nodes and relations, characteristic of the semantic network of concepts, has been carried out; the basic principles of constructing a semantic network of structural and linguistic concepts of recognition objects are formulated. Conclusions: To construct an image description that corresponds to the concept of semantic information processing and can be used in systems for collecting relevant images in computer systems and networks, it is advisable to use a structural-linguistic approach to recognition using a generalized semantic network of concepts. The use of this network in the classification and identification of objects can significantly expand the range of images accepted for consideration, taking into account different directions of shooting and different angles of camera deviation from nadir position.

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Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

Cited by 3 publications

References 26 publications

Research progress of content-based fabric image retrieval

Research progress of content-based fabric image retrieval

An efficient network based on double constrained loss for fabric image retrieval

Formation of a generalized semantic network of concepts

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