AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval

Monowar, Muhammad Mostafa; Hamid, Abd.; Ohi, Abu Quwsar; Alassafi, Madini O.; Mridha, M. F.

doi:10.3390/s22062188

Cited by 23 publications

(8 citation statements)

References 58 publications

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“…In 2022 Monowar, M.M., et al, [20] presented AutoRet, a DCNNimage retrieval system. A DCNN is used as the standard technique for extracting embeddings from various picture patches.…”

Section: Literature Reviewmentioning

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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“…In 2022 Monowar, M.M., et al, [20] presented AutoRet, a DCNNimage retrieval system. A DCNN is used as the standard technique for extracting embeddings from various picture patches.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval

et al. 2024

Preprint

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“…From Tables 1 and 2, we figured out that ResNet50 performs better than the other ResNet models for the CBTIR system with HD as an index for similarity measure calculation. https://www.indjst.org/ (12) ED 85 Monowar et al (15) ED 83 Abdullah (16) HD 98 Proposed ResNet50 ED 90 Proposed ResNet50…”

Section: Precision =mentioning

confidence: 99%

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

Anitha,

Kalaiselvi

2024

IJST

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Background/Objectives: The MRI has proven to be extremely effective in detecting tumors, with millions of images created each day throughout the world. To find similar images from a vast collection, Content-Based Tumor Image Retrieval (CBTIR) technology has been used to analysis the medical image. In the traditional retrieval methods, retrieving a similar image from the large database is crucial task. To overcome this issue we developed deep learning based retrieval method. Methods: This research offers a retrieval approach based on predefined ResNet models for quick and accurate image retrieval. We tested various prominent ResNet models with different distance similarity metrics, and the best option was determined by this work. Findings: After the various evaluation of ResNet models with varied distance measures on the CE-MRI data set, ResNet50 model applied with Hamming distance yields 99.33% of retrieval precision. Novelty: This work used predefined ResNet models with the combination of Distance similarity metrics to achieve more accurate results on medical image retrieval compared to the other conventional methods. Keywords: Content Based Image Retrieval, Tumor Retrieval, Hamming Distance, Euclidean Distance, Minkowski

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“…Monowar et al [21] introduced, a self-supervised image retrieval system using neural networks (NN), addressing the challenges of expensive or unfeasible data labeling. Trained on pairwise constraints, the model can function in self-supervised environments and with partially labeled datasets.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations

Battur,

Narayana

2023

IJACSA

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This paper proposes an innovative ensemble learning framework for classifying medical images using Support Vector Machine (SVM) and Fuzzy Logic classifiers. The proposed approach utilizes logical AND and OR operations to combine the predictions from the two classifiers, aiming to capitalize on the strengths of each. The SVM and Fuzzy Logic classifiers were independently trained on a comprehensive database of medical images comprising various types of X-ray images. The logical OR operation was then used to create an ensemble classifier that outputs a positive classification if either of the individual classifiers does so. On the other hand, the logical AND operation was used to construct an ensemble classifier that outputs a positive classification only if both individual classifiers do so. The proposed method aims to increase sensitivity and precision by capturing as many positive instances as possible, thereby reducing false positives. The scope of the proposed work is validated in terms of overall time complexity and retrieval accuracy. The simulation outcome shows promising result with 98.36 accuracy score and 1.8 seconds to retrieve all the images in query database.

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AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval

Cited by 23 publications

References 58 publications

Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval

Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations

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