Combining Multi-Feature Regions for FineGrained Image Recognition

Fayou, Sun; Ngo, Hea Choon; Sek, Yong Wee

doi:10.5815/ijigsp.2022.01.02

Cited by 15 publications

(11 citation statements)

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“…In that study, a novel fine-grained visual recognition model was established, i.e., a multifeature RA-CNN, which associates multiple feature regions to overcome the inefficiency of RA-CNN and improve the classification accuracy. Additionally, a feature scale-dependent algorithm was developed to improve the classification accuracy, and the performance of the developed algorithm was verified using the three most popular benchmarks: CUB-200-2011, Cars196, and Aircrafts100 [ 20 ]. In addition, object recognition model development using machine learning technology with multiple linear regression (MLR) has been investigated.…”

Section: Related Workmentioning

confidence: 99%

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

Lee

2023

Sensors

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In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning.

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Section: Related Workmentioning

confidence: 99%

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

Lee

2023

Sensors

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“…Using MATLAB image recognition system, reference ITTI opensource toolbox, in the test image random classification, combined with the database of color image capture object record change, the system will change the observation angle, light angle, and light color, to obtain folk arts and crafts images containing 24 light angle, 72 observation angle, and 12 light color [20]. To prove that the Gaussian hybrid model is also applicable to the bass model, each class of images contains three feature set orientation, color, light, some pictures as training Atlas, and the other images as test Atlas [21].…”

Section: Key Technologies Of Digital Display Of Folk Arts and Craftsmentioning

confidence: 99%

Research on Folk Handicraft Image Recognition Based on Neural Networks and Visual Saliency

Dai

2022

Mathematical Problems in Engineering

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How to identify quickly the images of folk arts and crafts works has become a difficult problem of cultural heritage value mining. Therefore, combined with the image recognition technology can improve the accuracy of the identification of folk arts and crafts works. This paper improves the ITTI significant model based on the method of linear addition of significant maps with the same proportion. Firstly, Bayesian model and Gaussian model are used to extract the probability distribution of image feature vector; secondly, the k-means algorithm is used to identify image accuracy extraction work, and finally ALOI database is used to test judgment image recognition accuracy; experimental results found that the improved technology does help to improve the folk arts and handicraft image recognition accuracy.

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“…However, currently, digital polarization microscopy methods for determining the age of injuries to human internal organs are practically absent in histological research. Moreover, the analysis of microscopic images is often performed semi-qualitatively through observation of the image structure by an expert, followed by subjective conclusions [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] . Given these limitations, it is crucial to develop and validate new methodologies for obtaining microscopic images, assessing their diagnostic effectiveness, and ensuring the accuracy of objective statistical analysis of polarization maps in microscopic images of histological sections from different types of injured human internal organs.…”

Section: Introductionmentioning

confidence: 99%

Methods and tools of forensic medical digital polarization histology of traumatized tissues of the deceased

Besaha,

Kinzerska,

Litvinenko

et al. 2024

Sixteenth International Conference on Correlation Optics

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Combining Multi-Feature Regions for FineGrained Image Recognition

Cited by 15 publications

References 0 publications

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

Research on Folk Handicraft Image Recognition Based on Neural Networks and Visual Saliency

Methods and tools of forensic medical digital polarization histology of traumatized tissues of the deceased

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