Crime Scene Investigation Image Retrieval with Fusion CNN Features Based on Transfer Learning

Liu, Ying; Peng, Yanan; Li, Daxiang; Fan, Jiulun; Li, Yun

doi:10.1145/3195588.3195605

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…They got an F1 score of 0.94 for Wikipedia and Twitter and 0.95 for the Form Spring dataset. Ying et al [44] aimed for a CNN-based image retrieval system for crime scene investigation. The suggested technique is based on the feature fusion technique, which exploits transfer learning to extract useful information from crime scenes.…”

Section: Deep Learning and Statistical Techniquesmentioning

confidence: 99%

Leveraging transfer learning with deep learning for crime prediction

Butt,

Letchmunan,

Hassan

et al. 2024

PLoS ONE

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Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

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Section: Deep Learning and Statistical Techniquesmentioning

confidence: 99%

Leveraging transfer learning with deep learning for crime prediction

Butt,

Letchmunan,

Hassan

et al. 2024

PLoS ONE

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“…The Authors have used three databases for the CBIR method that is ImageDB2000, Caltech101 and Image DBCorel dataset. Y. Liu et al in [26] proposed a technique based on CNN feature fusion for crime scene investigation image retrieval. The proposed method uses two different kinds of pre-trained VGG models for the feature extraction, and these features are then fused to generate the final feature map of the image.…”

Section: Content-based Medical Image Retrieval In Cloud Environmentmentioning

confidence: 99%

Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network

Tiwari

Pant

2022

ACM Trans. Multimedia Comput. Commun. Appl.

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In this paper, a brownfield Internet of Medical Things network is introduced for imaging data that can be easily scaled out depending on the objectives, functional requirements and the number of facilities and devices connected to it. This is further used to develop a novel Content-based Medical Image Retrieval framework. The developed framework uses DenseNet-201 architecture for generating the image descriptors. Then for classification, the optimized Deep Neural Network model has been configured through a population-based metaheuristic Differential Evolution. Differential Evolution iteratively performs the joint optimization of hyperparameters and architecture of Deep Neural Networks. The competence of the proposed model is validated on three publicly available datasets: Brain Tumor MRI dataset, Covid-19 Radiography database, and Breast Cancer MRI dataset and by comparing it with selected models over different aspects of performance evaluation. Results show that the convergence rate of the proposed framework is very fast, and it achieves at least 97.28% accuracy across all the models.

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“…With the remarkable success of CNN networks in various practical applications, there has been an abundance of methods for image retrieval based on CNN features. In [4,8,[13][14][15][16] , the author incorporates CNN features for fusion has significantly improved retrieval accuracy. By integrating multiple feature information, it is possible to better capture the semantic content, structural information, and contextual relationships of images, thereby improving the accuracy and robustness of image retrieval [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

An image retrieval method based on fusion of multiple features

Wang,

Dou,

Guo

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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In image retrieval tasks, two main indicators are focused on: accuracy and efficiency. In general, the more the number of features, the higher the retrieval accuracy but the lower efficiency. Focusing on ensuring retrieval efficiency while improving retrieval accuracy, this paper designs an unsupervised image retrieval system that integrates multiple features. On UKbench, Holidays, UC Merced Land Use, and RSSCN7, the method proposed was validated. On UKbench, compared to methods based on shuffleNetV2, VGG-16 and Resnet18, the method proposed improves N-S by 0.1882, 0.2499, and 0.1985 respectively, while saving retrieval time by 0.0115s. On Holidays, compared to methods based on shuffleNetV2, VGG-16 and Resnet18, the method proposed improves MAP by 7.11%, 9.05%, and 7.27% respectively, while saving retrieval time by 0.0021s. On UC Merced Land Use, compared to methods based on shuffleNetV2, VGG-16 and Resnet18, the method proposed improves accuracy by an average of 3.31%, 11.84%, and 6.29% respectively, while saving retrieval time by 0.0007s. On RSSCN7, compared to methods based on shuffleNetV2, VGG-16 and Resnet18, the method proposed improves accuracy by an average of 2.19%, 9.19%, and 7.18% respectively, while saving retrieval time by 0.0002s. The experimental results show that the proposed method can effectively improve retrieval accuracy and to some extent improve retrieval efficiency.

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Crime Scene Investigation Image Retrieval with Fusion CNN Features Based on Transfer Learning

Cited by 6 publications

References 19 publications

Leveraging transfer learning with deep learning for crime prediction

Leveraging transfer learning with deep learning for crime prediction

Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network

An image retrieval method based on fusion of multiple features

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