Multimedia image and video retrieval based on an improved HMM

Liu, Yanbing; Dhakal, Sanjev; Hao, Binyao

doi:10.1007/s00530-020-00686-1

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…is feature is consistent with hidden Markov model (HMM), which has been applied to stock price prediction by many scholars [17]. HMM is a statistical model that has been used in automatic speech recognition [18], DNA sequence analysis [19], image processing [20], and pattern recognition [21]. e main contribution of this paper is that the secondorder continuous HMM-based model is constructed for stock price prediction.…”

Section: Introductionmentioning

confidence: 72%

Research on HMM-Based Efficient Stock Price Prediction

2022

Mobile Information Systems

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Stock market is one of the most important parts of the investment market. Compared with other industries, the stock market not only has a higher rate of return on investment but also has a higher risk, and stock price prediction has always been a close concern of investors. Therefore, the research on stock price prediction methods and how to reduce the error of stock price prediction has become a hot topic for many scholars at home and abroad. In recent years, the development of computer technology such as machine learning and econometric method makes the stock price prediction more reliable. Due to the hidden Markov nature of stock price, this paper proposes a stock price prediction method based on hidden Markov model (HMM). To be specific, since the data of stock price have continuity in time series, it is necessary to extend the discrete HMM to the continuous HMM, and then put forward the up and down trend prediction model based on the continuous HMM. The first-order continuous HMM is extended to the second-order continuous HMM, and the stock price is predicted by combining the prediction method of fluctuation range. As a result, the proposed second-order continuous HMM-based stock price prediction model is simulated on Hang Seng Index (HSI), one of the earliest stock market indexes in Hong Kong. The evaluation results on six months HSI show that the predicted value of the proposed model is very close to the actual value and outperforms three benchmarks in terms of RMSE, MAE, and R2.

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Section: Introductionmentioning

confidence: 72%

Research on HMM-Based Efficient Stock Price Prediction

2022

Mobile Information Systems

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“…However, because there are a large number of interference shots in the tennis video on Data Set B, the error detection rate of these interference shots is relatively high. Therefore, compared with basketball and football, the classification effect of tennis video using convolutional neural network algorithm is slightly inferior [13]. At the same time, it also shows that the interference lens in the image will have a great impact on the image recognition.…”

Section: Single Sports Video Classification Experimentsmentioning

confidence: 95%

Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

Chen,

2023

ICST Transactions on Scalable Information Systems

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INTRODUCTION: Sports video is a very important information resource. The classification of sports video with high accuracy can effectively improve the browsing and query effect of users. This project intends to study a motion video classification algorithm based on deep learning particle filter to solve the problems of strong subjectivity and low accuracy of existing motion video classification algorithms. A key box extraction method based on similarity is proposed. The moving video classification algorithm based on deep learning coding model is studied. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can greatly improve the classification performance of motion video.

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“…The mathematical model of the illumination map T is shown in Equation (5). A modified weighted median filter is used to filter T. The weighted median filter uses the median value of the remaining pixels in the convolution window, except the pixel at the centre of the window, and replaces the pixel value at the centre of the window after weighting.…”

Section: Optimisation Of Illumination Map Tmentioning

confidence: 99%

“…The extracted edge image is expanded [37] as a detail region that satisfies the expression: (20) where the value of the convolutional kernel is set in Equation (20) to Equation (5).…”

Section: Identify the Level Of Detail At The Detail Concentration Areamentioning

confidence: 99%

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Image enhancement via texture protection Retinex

Dong

Zhao

Wang

2021

IET Image Processing

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Images obtained in dim light do not clearly represent the target scene, limiting information transmission on the image carrier. This study proposes a texture-preserving image enhancement method, i.e. ETPR. The proposed method draws the illumination map of a low light image by Max-RGB, and then applies the weighted median filter algorithm and Retinex to improve the illumination map further. Then, the enhanced image is changed from RGB mode to YCbCr mode and the texture of the luminance component Y is described. It is achieved by denoising the texture of the image to be enhanced, and then the final enhanced image is obtained by the method of sub-region image fusion denoising. The effectiveness of ETPR is established by comparing it with existing enhancement technologies using public data and real images.

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Multimedia image and video retrieval based on an improved HMM

Cited by 12 publications

References 15 publications

Research on HMM-Based Efficient Stock Price Prediction

Research on HMM-Based Efficient Stock Price Prediction

Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

Image enhancement via texture protection Retinex

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