A hierarchical approach based CBIR scheme using shape, texture, and color for accelerating retrieval process

Kumar, Sumit; Pal, Arup Kumar; Varish, Naushad; Nurhidayat, Irfan; Eldin, Sayed M.; Sahoo, Soubhagya Kumar

doi:10.1016/j.jksuci.2023.101609

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Compared to color and shape features, texture attributes are a more popular CBIR method. Two entirely different images with the same color histogram could exist [8]. The deep layer architecture extracts many styles and properties from an image using CNN, which have been demonstrated to be particularly effective for general image retrieval applications [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval

et al. 2024

Preprint

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Modern life is fast-paced, and every people is very busy with their daily routines. The online shopping option in E-commerce is a great time-saver in such a scenario. Moreover, it is crucial to extract accurate product features in order to search, recommend, classify, and retrieve images based on fashion queries.To overcome the forementioned challenges, a novel cloth swapping GAN based fashion retrieval has been introduced for rapid retrieval of relevant fashion based on the user query. Initially, to reduce the computational time, GrabCut is used to remove the background of the cloth images.The Cloth encoding decoding-based parsing Network is introduced to segment the bottom and top of the cloth. Then, the separated cloth region is fed into the GAN based on the user preference. The threshold neural network (TNN) is integrated with gates for efficient feature extraction in a small fraction of time. The feature extraction process is performed based on the feedback of the user. The extracted features such as dress length (long, medium, short), dress sleeve (sleeveless, full sleeve, half sleeve), and dress pattern (designs, dots, straights) are used to retrieve the relevant clothes for the users based on the query from the online shops. The proposed model achieves atotal accuracy of 99.29%. The proposed cloth retrieval system enhances the total accuracy by 14.24%, 8.75%, and 23.55% better than Alexnet, cGAN, and CNN, respectively.

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Section: Introductionmentioning

confidence: 99%

Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval

et al. 2024

Preprint

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Image retrievable encryption based on linear fitting and orthogonal transformation

Yang,

Zhang,

Jiang

et al. 2024

Phys. Scr.

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With the development of cloud computing, an increasing number of resource-constrained image owners tend to store their images in the cloud and rely on image retrieval services to obtain the images they desire. However, the security of the cloud cannot be fully guaranteed. To ensure image security while achieving good retrieval performance, we have designed a retrievable image encryption algorithm based on linear fitting and orthogonal transformation. This algorithm first generates encryption and feature extraction domains through orthogonal decomposition, and then applies a modified ResNet50 network for feature extraction in the feature extraction domain. The encryption process employs an improved affine transformation based on linear fitting, where part of the fitting values comes from the original image data and the other part comes from data generated by a chaotic system. Additionally, to simplify the measurement of feature similarity in the cloud, we have designed a hierarchical feature index tree to narrow the retrieval scope, thereby reducing retrieval complexity. Experimental results show that the proposed algorithm effectively protects image privacy and achieves high retrieval accuracy. The F-score reached 6.7634% on the Ghim10k dataset and 25.514% on the Corel 1K dataset, significantly improving upon traditional methods. This algorithm has potential application value in the fields of secure image storage and efficient retrieval in the cloud.

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Deep Neural Networks Based Feature Extraction with CBIR: An Exhaustive Analysis

2024

2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST)

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A hierarchical approach based CBIR scheme using shape, texture, and color for accelerating retrieval process

Cited by 4 publications

References 33 publications

Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval

Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval

Image retrievable encryption based on linear fitting and orthogonal transformation

Deep Neural Networks Based Feature Extraction with CBIR: An Exhaustive Analysis

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