Background Extraction Using Random Walk Image Fusion

Hua, Kai; Wang, Hong Cyuan; Yeh, Chih Hsiang; Cheng, Wen-Huang; Lai, Yu‐Chi

doi:10.1109/tcyb.2016.2640288

Cited by 13 publications

(3 citation statements)

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“…With the increasing prevalence of deep learning, a growing number of researchers in the field of medical imaging are currently focusing on integrating deep learning into their investigations [35]. For example, many recently proposed CAD systems for identifying pulmonary nodules utilize CNNs to achieve fast and accurate diagnoses.…”

Section: Related Workmentioning

confidence: 99%

Lung Cancer Classification using Reinforcement Learning-based Ensemble Learning

Luo

2023

IJACSA

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Lung cancer is a significant health issue affecting millions of people worldwide annually. However, current manual detection methods used by physicians and radiologists to identify lung nodules are inefficient because of the diverse shapes and locations of the nodules in the lungs. New methods are needed to improve the accuracy and speed of detecting lung nodules. This is important because early detection of nodules can increase the likelihood of successful treatment and recovery. This paper introduces a new LLC-QE model that combines ensemble learning and reinforcement learning to classify lung cancer. Initially, the model undergoes pre-training through the utilization of the Artificial Bee Colony (ABC) algorithm. This approach aims to decrease the probability of the model getting stuck in a local optimum. Subsequently, a set of convolutional neural networks (CNNs) is used to simultaneously derive feature vectors from input images, which are subsequently combined for classification in downstream processes. The LIDC-IDRI dataset, predominantly composed of cases without cancer, was employed to train and evaluate the model. To mitigate the dataset imbalance, the training procedure using reinforcement learning is formulated as a series of interconnected decisions. During this process, the images are regarded as states; the network acts as the agent, and the agent is given a greater reward/punishment for accurately/incorrectly classifying the underrepresented class compared to the overrepresented class. The LLC-QE model achieves excellent results (F measure 89.8%; geometric mean 92.7%), outperforming other deep models. Identifying the optimal values for the reward function and determining the ideal number of CNN feature extractors in the ensemble are achieved through experiments conducted on the study dataset. Ablation studies that exclude ABC pre-training and reinforcement learning from the model confirm these components' independent positive incremental impact on the model's performance.

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Section: Related Workmentioning

confidence: 99%

Lung Cancer Classification using Reinforcement Learning-based Ensemble Learning

Luo

2023

IJACSA

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“…The topic of background modeling is vast and has been widely researched by many scholars [9], [10], [13]- [19], [21]- [34]. Different terms have been used to refer to background modeling, including background estimation, bootstrapping, background initialization, background generation and background reconstruction etc [20].…”

Section: Related Workmentioning

confidence: 99%

A Novel Background Modeling Based on Keyframe and Particle Shape Property for Surveillance Video

Fan,

He,

Lin

et al. 2023

IEEE Access

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With the development of industrial informatization, video processing technology is receiving more and more attention. Extracting background is a prerequisite for many video processing techniques, so video background modeling technology is becoming highly sought-after. Currently, there are a variety of approaches to estimating background; however, many of these methods have the fault of not being able to accurately distinguish between foreground and background, especially when objects move slowly or remain still for a period of time. In this paper, a novel background modeling scheme is proposed for surveillance video, based on keyframe and particle shape properties. The model consists of three parts: the first part is to reduce running time and eliminate the ghost phenomenon caused by adjacent redundant frames by extracting keyframe and dividing the extracted frames into several groups; the second part includes three steps, computing binarized difference, characterizing the binarized difference and screening the difference where a quadruple, composed of particle shape properties, is designed to quantitatively describe binarized differences; the third part involves generating the temporary background and updating the temporary background according to the similarity of data obtained at the newly proposed locations. Experiment results on SBMnet and SBI datasets and comparisons with some emerging algorithms show that the performance of the proposed model is superior or comparable to the other state-of-the-art methods particularly when dealing with stationary objects. Furthermore, the proposed method ranks as the second for intermittent category video, compared to the other 31 state-of-art methods. Moreover, the speed of the proposed method, 5.38 Frame Per Second ( FPS ) for SBMnet dataset and 10.94 FPS for SBI dataset, is faster than most public methods.INDEX TERMS video processing, surveillance video, background modeling, difference, quadruple, keyframe, particle shape, stationary object.

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“…However, it may fail to recover a background patch if a foreground object stays stationary in several frames. In addition, all patch-based methods may result in blocking artifacts and seams [13] .…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Background Recovery from Multiple Images

Gao

Huang²,

Maier³

2019

Preprint

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In this paper, we propose an intuitive method to recover background from multiple images. The implementation consists of three stages: model initialization, model update, and background output. We consider the pixels whose values change little in all input images as background seeds. Images are then segmented into superpixels with simple linear iterative clustering. When the number of pixels labelled as background in a superpixel is bigger than a predefined threshold, we label the superpixel as background to initialize the background candidate masks. Background candidate images are obtained from input raw images with the masks. Combining all candidate images, a background image is produced. The background candidate masks, candidate images, and the background image are then updated alternately until convergence. Finally, ghosting artifacts is removed with the k-nearest neighbour method. An experiment on an outdoor dataset demonstrates that the proposed algorithm can achieve promising results.

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Background Extraction Using Random Walk Image Fusion

Cited by 13 publications

References 50 publications

Lung Cancer Classification using Reinforcement Learning-based Ensemble Learning

Lung Cancer Classification using Reinforcement Learning-based Ensemble Learning

A Novel Background Modeling Based on Keyframe and Particle Shape Property for Surveillance Video

Superpixel-Based Background Recovery from Multiple Images

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