Grading image retrieval based on CNN deep features

Luo, Yiwen; Li, Y; Han, Fangjian; Huang, Shucai

doi:10.23919/icact.2018.8323677

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Preprocessing is performed to remove distortions and other unwanted features while processing the image and extract the proper portion of the image corresponding to the analysis of image retrieval using different algorithms [ 25 – 27 ] such as boundary detection. Preprocessing involves removing unwanted features, resizing the image, boundary detection, and normalization.…”

Section: Proposed Methodologymentioning

confidence: 99%

[Retracted] Object‐Based Image Retrieval Using the U‐Net‐Based Neural Network

Kumar

Jain

Agarwal

et al. 2021

Computational Intelligence and Neuroscience

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Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.

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Section: Proposed Methodologymentioning

confidence: 99%

[Retracted] Object‐Based Image Retrieval Using the U‐Net‐Based Neural Network

Kumar

Jain

Agarwal

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The qualified CNN is well adapted to solve grouping, identification, and prediction tasks with highly efficient adaptability on test results. The CNN foretelling signals' efficiency has exceeded that of the rule-based dataset features [24,25]. CNN can also be understood as a features extractor for converting the image into a compression representation function space, which helps manipulate images.…”

Section: Neural Network Especiallymentioning

confidence: 99%

Image Compression Based on Deep Learning: A Review

Yasin

Abdulazeez

2021

AJRCoS

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Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.

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An intelligent content-based image retrieval methodology using transfer learning for digital IP protection

Trappey

Shih

2021

Advanced Engineering Informatics

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Grading image retrieval based on CNN deep features

Cited by 3 publications

References 14 publications

[Retracted] Object‐Based Image Retrieval Using the U‐Net‐Based Neural Network

[Retracted] Object‐Based Image Retrieval Using the U‐Net‐Based Neural Network

Image Compression Based on Deep Learning: A Review

An intelligent content-based image retrieval methodology using transfer learning for digital IP protection

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