Defining a deep neural network ensemble for identifying fabric colors

Amelio, Alessia; Bonifazi, Gianluca; Corradini, Enrico; Saverio, Simone Di; Marchetti, Michele; Ursino, Domenico; Virgili, Luca

doi:10.1016/j.asoc.2022.109687

Cited by 21 publications

(6 citation statements)

References 33 publications

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“…hoc feature extraction methods (including fractal analysis, wavelet coefficients, and edge detection) to represent visual artistic features such as brushstrokes, followed by a machine learning model trained on such features to distinguish the works of the artist from possibly similar works by other artists [4,5,6,7,8]. The excellent pattern-recognition abilities of Convolutional Neural Networks (CNNs) have led to a new wave of studies showing impressive performances on art-classification tasks [9,10,11] and many other visual tasks [12,13,14]. These studies involve complex CNN architectures that are trained on large digitized art collections, generally adding to the CNN a last dense (fully-connected) layer.…”

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confidence: 99%

Art authentication with vision transformers

Schaerf,

Postma,

Popovici

2023

Neural Comput & Applic

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In recent years, transformers, initially developed for language, have been successfully applied to visual tasks. Vision transformers have been shown to push the state of the art in a wide range of tasks, including image classification, object detection, and semantic segmentation. While ample research has shown promising results in art attribution and art authentication tasks using convolutional neural networks, this paper examines whether the superiority of vision transformers extends to art authentication, improving, thus, the reliability of computer-based authentication of artworks. Using a carefully compiled dataset of authentic paintings by Vincent van Gogh and two contrast datasets, we compare the art authentication performances of Swin transformers with those of EfficientNet. Using a standard contrast set containing imitations and proxies (works by painters with styles closely related to van Gogh), we find that EfficientNet achieves the best performance overall. With a contrast set that only consists of imitations, we find the Swin transformer to be superior to EfficientNet by achieving an authentication accuracy of over 85%. These results lead us to conclude that vision transformers represent a strong and promising contender in art authentication, particularly in enhancing the computer-based ability to detect artistic imitations.

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confidence: 99%

Art authentication with vision transformers

Schaerf,

Postma,

Popovici

2023

Neural Comput & Applic

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“…In Table III, the relative outputs of the OANN-FDDC models are compared with those of recent approaches [27,28]. The simulation outcome indicates that the CNN, ResNet50v2, and DenseNet169v2 models gave poor performance, whereasFPN, Bi-FPN, NAS-FPN, and Dense-FPN models reported moderately greater outputs.…”

Section: Resultsmentioning

confidence: 99%

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Sajitha,

Priya

2024

Eng. Technol. Appl. Sci. Res.

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Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

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“…For example, recommending the appropriate developer for the required software development project [21,22], investigating the developer's history [23,24], figuring out other contributing success factors of the CCSD projects [14,25], developing simulation methods for failure prediction and task scheduling [26,27], success prediction in the CCSD [4], and quality assessment [28,29]. Authors [30,31] provided significant contributions to this field, specifically in the context of using deep learning for project success prediction. However, only limited research has been conducted on automatic and immediate CSP success prediction using Bidirectional Encoder Representations from Transformers (BERT).…”

Section: Introductionmentioning

confidence: 99%

Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

Rashid,

Anwar,

Jaffar

et al. 2024

Applied Sciences

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Competitive Crowdsourcing Software Development (CCSD) is popular among academics and industries because of its cost-effectiveness, reliability, and quality. However, CCSD is in its early stages and does not resolve major issues, including having a low solution submission rate and high project failure risk. Software development wastes stakeholders’ time and effort as they cannot find a suitable solution in a highly dynamic and competitive marketplace. It is, therefore, crucial to automatically predict the success of an upcoming software project before crowdsourcing it. This will save stakeholders’ and co-pilots’ time and effort. To this end, this paper proposes a well-known deep learning model called Bidirectional Encoder Representations from Transformers (BERT) for the success prediction of Crowdsourced Software Projects (CSPs). The proposed model is trained and tested using the history data of CSPs collected from TopCoder using its REST API. The outcomes of hold-out validation indicate a notable enhancement in the proposed approach compared to existing methods, with increases of 13.46%, 8.83%, and 11.13% in precision, recall, and F1 score, respectively.

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Defining a deep neural network ensemble for identifying fabric colors

Cited by 21 publications

References 33 publications

Art authentication with vision transformers

Art authentication with vision transformers

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

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