Evolutionary Optimization Algorithm on Content based Image Retrieval System using Handcrafted features with Squeeze Networks

Karthik, T.; Krishna, R. V. V.; Rao, T. K. Ramakrishna; Manoranjithem, V.; Kalaiarasi, S.; Jegajothi, B.

doi:10.1109/icais53314.2022.9742766

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…SqueezeNet, MobileNet, and ShuffleNet were used to construct a classification model for the rapid, intelligent identification of various PAHs. SqueezeNet is a lightweight network based on a model-compression strategy [ 37 ]. The structure of the SqueezeNet used in this study is shown in Figure S3 in the Supporting Information .…”

Section: Methodsmentioning

confidence: 99%

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

Qiu,

Tang,

Wang

et al. 2023

Foods

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The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling β-cyclodextrin-modified gold nanoparticles (β-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (β-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 μg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the β-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field.

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Section: Methodsmentioning

confidence: 99%

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

Qiu,

Tang,

Wang

et al. 2023

Foods

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show abstract

“…This could also refer to other images of texture with strong directional properties. Depending on the specific pattern, the image retrieval system has wide-ranging applications in fashion e-commerce sites as a sorting and filtering method [29] for different fabric patterns [30][31][32].…”

Section: Literature Surveymentioning

confidence: 99%

Content based word image retrieval using multi-layer perception based convolutional neural networks

Gopatoti¹

2023

IJSMEM

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Content based image retrieval (CBIR) plays the major role in real time applications likes search engines, libraries. The conventional CBIR systems are implemented by using basic image processing and machine learning models. So, they resulted in the poor performance against various situations. Therefore, this article is focused on implementation of CBIR system using multi-layer perception based convolutional neural network (MLP-CNN). Initially, principal component analysis (PCA) applied on images to perform the dimensionality reduction operation, which also extracts the content specific features. Further, MLP-CNN model is used to train the system and generates the trained features. Finally, the testing operation is performed using MLP-CNN, which generates the output as content specific images. The simulation results shows that the proposed method resulted in superior performance as compared to state of art approaches.

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“…The proposed method has applied on the the three color texture image datasets VisTex, Color Brodatz and Stex, and the three natural image datasets Corel-1 K, Corel-10 K and Corel-5 K. Yelchuri, Rajesh, et al in (2022) [27] proposed deep and hand-crafted features for constructing new texture image retrieval systems by many experiments conducted to extract deep features by many popular Convolutional Neural Network architectures (AlexNet, InceptionV3, VGG16, and ResNet50) to predict the class membership of the query image to every output class. Also, the similarities of these images and every images in the database are calculated by a adjusted distance matrix in the hand-crafted space wavelet features by normalized city-block Karthik, T. S., et al in (2022) [28] they propose (EOCBIR-HFSN) approach using handcraft features based on local binary patterns (LBP) and SqueezeNet-based deep features. In addition, the grasshopper optimization process is used to fine-tune the SqueezeNet model's hyperparameters (GOA).…”

Section: Deep Learningmentioning

confidence: 99%

An Overview of Content-Based Image Retrieval Methods and Techniques

Hadid,

Hussein,

Al-Qaysi

et al. 2023

ijcsm

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With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval

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Evolutionary Optimization Algorithm on Content based Image Retrieval System using Handcrafted features with Squeeze Networks

Cited by 5 publications

References 17 publications

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

Content based word image retrieval using multi-layer perception based convolutional neural networks

An Overview of Content-Based Image Retrieval Methods and Techniques

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