Feature Extraction using Self-Supervised Convolutional Autoencoder for Content based Image Retrieval

Siradjuddin, Indah Agustien; Wardana, Wrida Adi; Sophan, Mochammad Kautsar

doi:10.1109/icicos48119.2019.8982468

Cited by 17 publications

(4 citation statements)

References 8 publications

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“…Saritha et al [ 22 ] proposed a deep belief network method using deep learning to extract image feature information for a large amount of generated data. Siradjuddin et al [ 23 ] used the feature learning capabilities of convolutional neural networks to extract important representations of images and reduce the dimensionality of the images and used the neural network to mine the content information of the image. Since the complexity of the network is positively related to the depth, the deeper the network, the higher the complexity and the more abstract the content feature images obtained, and the content features of the image are difficult to retain.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Neural Network‐Based Mapping Mining of Image Style Transfer in Big Data Systems

Zheng

et al. 2021

Computational Intelligence and Neuroscience

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Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model’s perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people’s requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Neural Network‐Based Mapping Mining of Image Style Transfer in Big Data Systems

Zheng

et al. 2021

Computational Intelligence and Neuroscience

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“…Siradjuddin et al [52], in Content-based Image Retrieval, provided an autoencoder using a Convolutional Neural Network for feature extraction. In the convolutional autoencoder architecture, the encoder and decoder layers are used.…”

Section: Literature Reviewmentioning

confidence: 99%

An analysis of content-based image retrieval

Koyuncu

Dixit²,

Koyuncu

2021

International Advanced Researches and Engineering Journal

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Nowadays, working on digital images is gaining much popularity in multimedia systems, due to the rapid increase in the utilization of large image databases. Thus, the Content-Based Image Retrieval (CBIR) method has become the most valuable method for these databases. This study mainly focuses on content-based image retrieval; which uses image features like color, shape, texture, etc. by searching the user query image from a large image database based on user request. CBIR is the most widely used technique as its searching capability is faster than the other traditional methods, and it works well in retrieving images automatically. It is also a big alternative approach to traditional methods. The CBIR techniques are used in many applications like surveillance detection, crime avoidance, fingerprint identification, E-library, medical, historical monument and biodiversity information systems, and many more. A total of 38 CBIR articles were comparatively analyzed.

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“…autoencoder(AE) [24] and variation autoencoder(VAE) [25], work well for feature extraction. These methods achieve great success in generating abstract features with high di-mensional data [26] [27]. Low-dimensional semantic space can be extracted by unsupervised-learning-based feature extraction methods from high-dimensional features by VAE-tSNE (variational autoencoder stochastic neighbor embedding) method [28].…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Method for Optical Thin-Film Design

Jiang

Chen

Yoshie

2022

IEICE Trans. Electron.

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Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design. The vast majority of this research has focused on the parameter optimization (layer thickness, and structure size) of optical thin-films. A challenging problem that arises is an automated material search. In this work, we propose a new end-to-end algorithm for optical thin-film inverse design. This method combines the ability of unsupervised learning, reinforcement learning and includes a genetic algorithm to design an optical thin-film without any human intervention. Furthermore, with several concrete examples, we have shown how one can use this technique to optimize the spectra of a multi-layer solar absorber device.

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Feature Extraction using Self-Supervised Convolutional Autoencoder for Content based Image Retrieval

Cited by 17 publications

References 8 publications

[Retracted] Neural Network‐Based Mapping Mining of Image Style Transfer in Big Data Systems

[Retracted] Neural Network‐Based Mapping Mining of Image Style Transfer in Big Data Systems

An analysis of content-based image retrieval

A Reinforcement Learning Method for Optical Thin-Film Design

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