Content-based image retrieval for fabric images: A survey

Tena, Silvester; Hartanto, Rudy; Ardiyanto, Igi

doi:10.11591/ijeecs.v23.i3.pp1861-1872

Cited by 7 publications

(9 citation statements)

References 45 publications

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“…Compared with KBIR, CBIR is more objective 3 – 5 and uses image content to retrieve images to avoid the influence of human subjectivity on the result. It has gathered the attention of researchers across several disciplines, like fabric and fashion design 6 , art galleries, remote sensing, and medical imaging. The first commercial version of the CBIR system was created by I.B.M., named query by image content (QBIC) 7 .…”

Section: Introductionmentioning

confidence: 99%

Content-based image retrieval of Indian traditional textile motifs using deep feature fusion

Varshney,

Singh,

Lakshmi

et al. 2024

Sci Rep

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In the fast-paced fashion world, unique designs are like early birds, grabbing attention as online shopping surges. Fabric texture plays an immense role in selecting the perfect design. Indian Traditional textile motifs are pivotal, showing rich cultural origins and attracting worldwide art fanatics. Yet, technology-driven abstract forms are posing a challenge for them. The decline of handmade artistic ability due to computerization is concerning. Crafting new designs associated with the latest trends is time- consuming and requires diligence. In this work an interactive CBIR (content-based image retrieval) system is presented. It utilizes deep features from InceptionV3 and InceptionResNetV2 models to match query designs with a database of traditional Indian textiles. Its performance is tested with Caltech-101, Corel-1K state-of-the-art datasets, and Indian Textiles datasets and the results are shown to be finer than the existing approaches. The similarity-based fine-grained saliency maps (SBFGSM) approach is employed to visualize the importance of features. Our approach combines deep feature fusion with PCA dimensionality reduction and speeds up search using a clustering approach. Relevance feedback is employed to refine the retrievals. This tool is expected to benefit designers by accelerating the design cycles by bridging the gap between human creativity and A.I. assistance.

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Section: Introductionmentioning

confidence: 99%

Content-based image retrieval of Indian traditional textile motifs using deep feature fusion

Varshney,

Singh,

Lakshmi

et al. 2024

Sci Rep

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“…CNNs are widely used for image recognition, pattern recognition, and speech recognition. Color, texture, shape, and spatial information can be used as features to create an algorithm [ 3 , 4 , 5 ]. Several image retrieval studies have been conducted on various datasets using pretrained CNN models with transfer learning methods.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the image retrieval process, pretrained CNNs have been used to extract features in various reports to overcome the semantic gap and manage large datasets [ 3 , 5 , 8 ]. A pretrained CNN model has been reported to provide a higher retrieval accuracy than handcrafted feature extraction techniques [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval for Traditional Indonesian Woven Fabric Images Using a Modified Convolutional Neural Network Method

Tena,

Hartanto,

Ardiyanto

2023

J. Imaging

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A content-based image retrieval system, as an Indonesian traditional woven fabric knowledge base, can be useful for artisans and trade promotions. However, creating an effective and efficient retrieval system is difficult due to the lack of an Indonesian traditional woven fabric dataset, and unique characteristics are not considered simultaneously. One type of traditional Indonesian fabric is ikat woven fabric. Thus, this study collected images of this traditional Indonesian woven fabric to create the TenunIkatNet dataset. The dataset consists of 120 classes and 4800 images. The images were captured perpendicularly, and the ikat woven fabrics were placed on different backgrounds, hung, and worn on the body, according to the utilization patterns. The feature extraction method using a modified convolutional neural network (MCNN) learns the unique features of Indonesian traditional woven fabrics. The experimental results show that the modified CNN model outperforms other pretrained CNN models (i.e., ResNet101, VGG16, DenseNet201, InceptionV3, MobileNetV2, Xception, and InceptionResNetV2) in top-5, top-10, top-20, and top-50 accuracies with scores of 99.96%, 99.88%, 99.50%, and 97.60%, respectively.

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“…8 In the textile field, Tena et al. 9 surveyed CBIR technologies from the perspective of feature extraction methods and grouped them into traditional methods and CNN-based methods, but ignored other technologies like similarity measurement and retrieval strategy. Furthermore, the review involved the fabric and clothing image retrieval methods which are quite different.…”

mentioning

confidence: 99%

“…Differently, the reviews of CBIR in different application fields are conducted to survey the application and improvement of technologies based on the characteristics of the images in each field, like the medical image 7 and the remote sensing image. 8 In the textile field, Tena et al 9 surveyed CBIR technologies from the perspective of feature extraction methods and grouped them into traditional methods and CNN-based methods, but ignored other technologies like similarity measurement and retrieval strategy. Furthermore, the review involved the fabric and clothing image retrieval methods which are quite different.…”

mentioning

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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Content-based image retrieval for fabric images: A survey

Cited by 7 publications

References 45 publications

Content-based image retrieval of Indian traditional textile motifs using deep feature fusion

Content-based image retrieval of Indian traditional textile motifs using deep feature fusion

Content-Based Image Retrieval for Traditional Indonesian Woven Fabric Images Using a Modified Convolutional Neural Network Method

Research progress of content-based fabric image retrieval

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