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4th IEEE International Conference on Broadband Network and Multimedia Technology (IEEE IC-BNMT 2011)

作者： Liu, Jie Du, Junping Wang, Xiaoru Song, Yang Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China

ISBN: (纸本)9781612841564

This paper proposes a hybrid algorithm based on improved lle and adaptive k-means for visual codebook generation in tourism scene classification. Firstly, we construct the improved lle algorithm to get lower dimensional and compressed image feature representations. Then we form the adaptive k-means clustering algorithm to generate the visual codebook. Finally, we use the visual codebook histogram to represent the samples and train the SVM classifier for scene classification task. Experiments are conducted on a Beijing tourism scene dataset to evaluate the performance of the hybrid algorithm. Experimental results show that our algorithm can effectively improve the robustness of the visual codebook and result in a satisfying performance of scene classification.

关键词： lle algorithm k-means clustering visual codebook scene classification

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A HYBRID algorithm BASED ON IMPROVED lle AND K-MEANS FOR VISUAL CODEBOOK GENERATION IN SCENE CLASSIFICATION

A HYBRID ALGORITHM BASED ON IMPROVED LLE AND K-MEANS FOR VIS...

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2011 4th IEEE International Conference on Broadband Network & Multimedia Technology(第四届IEEE宽带网络与多媒体国际会议 4th IEEE IC-BNMT2011)

作者： Jie Liu Junping Du Xiaoru Wang Yang Song Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China

关键词： lle algorithm k-means clustering visual codebook scene classification

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A HYBRID algorithm BASED ON IMPROVED lle AND K-MEANS FOR VISUAL CODEBOOK GENERATION IN SCENE CLASSIFICATION

A HYBRID ALGORITHM BASED ON IMPROVED LLE AND K-MEANS FOR VIS...

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2011 4th IEEE International Conference on Broadband Network and Multimedia Technology

This paper proposes a hybrid algorithm based on improved lle and adaptive k-means for visual codebook generation in tourism scene ***,we construct the improved lle algorithm to get lower dimensional and compressed image feature *** we form the adaptive k-means clustering algorithm to generate the visual ***,we use the visual codebook histogram to represent the samples and train the SVM classifier for scene classification *** are conducted on a Beijing tourism scene dataset to evaluate the performance of the hybrid *** results show that our algorithm can effectively improve the robustness of the visual codebook and result in a satisfying performance of scene classification.

关键词： lle algorithm k-means clustering visual codebook scene classification

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Comparison of lle and PCA algorithms for Gene Expression Data Analysis

Comparison of LLE and PCA Algorithms for Gene Expression Dat...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Chen, Xiaozhou Yang, Fan Li, Huamei Chen, Junhua Yunnan Univ Nationalities Sch Math & Comp Sci Kunming 650031 Peoples R China

ISBN: (纸本)9783038350125

According to the problem that the linear dimension reduction is not effective to understand gene expression data. using the manifold learning as a guide, analysing dimensionality reduction of gene expression data, selecting colon cancer and leukaemia gene expression datasets for investigation, using inter category distances as the criteria to quantitatively evaluate the effects of data dimensionality reduction. Experiments show that lle algorithm is more suitable method for the gene expression data. The lle analyses indicate that there is a clear distinction boundary between the healthy people and the cancer patients.

关键词： lle algorithm PCA algorithm dimensional reduction gene expression Data

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Stock price forecasting based on Hausdorff fractional grey model with convolution and neural network

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第4期18卷 3323-3347页

作者： Dong, Wenhua Zhao, Chunna Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China

Forecast of stock prices can guide investors' investment decisions. Due to the high dimensional and long-memory characteristics of stock data, it is difficult to predict. The fractional grey model with convolution (FGMC (1, m)) can be used to predict time series, because of its memory and ability to process high-dimensional data. However, the FGMC (1, m) model has some disadvantages, including complex calculation, loss of information, and approximate background values. In this paper, Hausdorff fractional derivative and Newton-Cotes formula are used to optimize these shortcomings and can get a Hausdorff fractional grey model with convolution (HFGMC (1, m)) model. The HFGMC (1, m)-lle-BP model is proposed in this paper. HFGMC (1, m) provides a solution that can reduce the complexity of the cumulative generator matrix calculation and preserve the global information of the sequence. Newton-Cotes formula is used to calculate the background value, which can solve the shortcomings of approximate background values. The HFGMC (1, m) model is used to predict the linear component of the sequence, and the BP neural network is used to predict the nonlinear component of the sequence. In addition, because of the high-dimensional and nonlinear characteristics of stock data, a local linear embedding (lle) algorithm is used to remove redundant information in high-dimensional non-linear data. The experimental results show that the HFGMC (1, m)-lle-BP model is effective for predicting the stock price in different trends.

关键词： stock price forecasting FGMC (1 m) Hausdorff fractional derivative sewton-cotes formula lle algorithm BP neural network

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A SUPERPIXEL-BASED AUTOMATIC CLASSIFICATION METHOD FOR POLARIMETRIC SAR IMAGE

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2019年第6期34卷 619-626页

作者： Han, Jinghong Wang, Haijiang Gao, Mengqing Sun, Min Chengdu Univ Informat Technol Coll Elect Engn Chengdu 610225 Peoples R China

To improve the classification accuracy of polarimetric synthetic aperture radar (PolSAR) images, a classification of algorithm based on superpixel is proposed, and the locally linear embedding (lle) dimension reduction algorithm is improved in the process of reducing the feature dimension. The traditional image classification methods are based on pixel, and the classification effect is not satisfactory. The classification method proposed in this paper is based on superpixel segmentation combined with majority voting algorithm and Wishart algorithm. This method is superior to traditional algorithms. The lle algorithm is improved, and the distance metric combining the Wishart distance with the Euclidean distance is proposed. This method makes the dimension reduce data more favourable for classification. Experimental results of two PolSAR images are presented in this paper. The results show that the proposed method is superior to the traditional method and can achieve better classification effect.

关键词： Polarimetric SAR lle algorithm majority voting Wishart superpixel classification

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Application of artificial intelligence technology in recognition of sports athletes' running foul

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第6期22卷 2051-2063页

作者： Xie, Zhicheng Ren, Shanchang Qie, Yushi Yanching Inst Technol Sch Phys Educ Langfang 065201 Hebei Peoples R China Shangqiu Inst Technol Phys Educ Dept Shangqiu Henan Peoples R China

In order to solve the problems of low recognition efficiency, low recognition rate and large recognition error of traditional methods, an application method of artificial intelligence technology in athletes' running foul recognition was proposed. Build the image acquisition model of sports athletes' running foul, divide each frame of the image samples into static area and motion area, and get the motion direction estimation results;K-means in the field of artificial intelligence is used to cluster the characteristics of sports athletes' rush foul action, and lle algorithm is used to reduce the dimension of features;The background subtraction method is used to detect the foul target of rush, and the Bayesian algorithm is used to construct the recognition model of sports athletes' foul of rush, which is used to identify the foul target. The experimental results show that the recognition rate of this method has reached more than 72%, and continues to increase, and the recognition error is only 2%, which effectively improves the recognition rate and reduces the recognition error, which is feasible and effective.

关键词： Artificial intelligence foul identification K-means clustering feature dimensionality reduction lle algorithm background subtraction static area dynamic area

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