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Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation

电子学报(英文版) 2010年第3期19卷 451-456页

作者： LI Bo LIU Wenju DOU Lihua School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling. Finite Gaussian mixture model is usually used in practice and the selection of number of mixture components is a significant problem in its application. For example, in image segmentation, it is the donation of the number of segmentation regions. The determination of the optimal model order therefore is a problem that achieves widely attention. This paper proposes a degenerating model algorithm that could simultaneously select the optimal number of mixture components and estimate the parameters for Gaussian mixture model. Unlike traditional model order selection method, it does not need to select the optimal number of components from a set of candidate models. Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it select the correct model order in a different way that needs less operation times and less sensitive to the initial value of EM. The experimental results show the effectiveness of the algorithm.

关键词：高斯混合模型图像分割无监督学习应用概率密度函数椭球等高分布模型估计高斯密度

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Bioimage-based protein subcellular location prediction： a comprehensive review

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Frontiers of Computer Science 2018年第1期12卷 26-39页

作者： Ying-Ying XU Li-Xiu YAO Hong-Bin SHEN Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.

关键词： bioimage informatics protein subcellular loca-tion prediction global and local features multi-location pro-tein recognition

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An image matching evolutionary algorithm based on Hu invariant moments

An image matching evolutionary algorithm based on Hu invaria...

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3rd International Conference on Image Analysis and Signal Processing, IASP 2011

作者： Zhang, Ruliang Wang, Lin Key Laboratory of Pattern Recognition and Intelligent Control Guizhou University for Nationalities Guiyang China

ISBN: (纸本)9781612848808

This paper presents an image matching evolutionary algorithm (called IMEA algorithm) based on Hu invariant moments. First, the population is initialized. A group of searched subgraphs is constructed. Second, the fitness function based on Hu invariant moments is designed. The seven Hu invariant moments of the template image and the searched subgraph are calculated. The Euclidean distance of Hu invariant moments is used to measure similarity between the template image and the searched subgraph. The template image and the searched subgraph are matched if these Euclidean distances are less than the set threshold. Finally, a new searched subgraph is constructed by means of a new evolutionary strategy. The new searched subgraph replaces the searched subgraph whose value of the fitness function is maximum. Experimental results demonstrate the great robustness and efficiency of the IMEA algorithm. © 2011 IEEE.

关键词： Evolutionary algorithms

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Sparse feature representation for visual tracking

Sparse feature representation for visual tracking

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2012 International Conference on systems and Informatics, ICSAI 2012

作者： Liu, Yifei Han, Zhenjun Ye, Qixiang Jiao, Jianbin Li, Ce Pattern Recognition and Intelligent System Development Laboratory Graduate University Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467301992

In this paper, a novel sparse feature representation method for object tracking is proposed. The method is on the observation that a tracked object can be dynamically and compactly represented by a few features (sparse representation) from a large feature set (the improved histogram of oriented gradient and color, HOGC). Based on the HOGC features, the sparse representation can be learned online from the constructed training samples during the tracking procedure by exploiting the L1-norm minimization principle, which can also be called feature selection procedure, ensuring the tracking can adapt to the appearance variations of either foreground or background. Experiments with comparisons demonstrate the effectiveness of the proposed method. © 2012 IEEE.

关键词： Feature Selection

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Voice-based local search using a language model look-ahead structure for efficient pruning

Voice-based local search using a language model look-ahead s...

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2011 7th International Conference on Computational Intelligence and Security, CIS 2011

作者： Lu, Yao Liu, Gang Chen, Wei Olsen, Jesper Pattern Recognition and Intelligent System Laboratory BUPT Beijing China Nokia Research Center NOKIA Beijing China

ISBN: (纸本)9780769545844

On mobile terminals, voice-based local search services [1] are quickly becoming a new important application. Voice search is essentially a large vocabulary speech recognition task with an open ended vocabulary, and this is a problem because speed and accuracy are essential for a good user experience. Fortunately when a user submits a local search query, contextual information such as the user's current position can be used for constraining the full search space. In this paper, we use local information and present a pruning algorithm based on a LMLA (Language Model Look-Ahead) tree, which can significantly improve both the speed and the accuracy of the voice search system. © 2011 IEEE.

关键词： Trees (mathematics)

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KNOWLEDGE TRANSFER BASED FINE-GRAINED VISUAL CLASSIFICATION

KNOWLEDGE TRANSFER BASED FINE-GRAINED VISUAL CLASSIFICATION

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhang, Siqing Du, Ruoyi Chang, Dongliang Ma, Zhanyu Guo, Jun The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665438643

Fine-grained visual classification (FGVC) aims to distinguish the sub-classes of the same category and its essential solution is to mine the subtle and discriminative regions. Convolution neural networks (CNNs), which employ the cross entropy loss (CE-loss) as the loss function, show poor performance since the model can only learn the most discriminative part and ignore other meaningful regions. Some existing works try to solve this problem by mining more discriminative regions by some detection techniques or attention mechanisms. However, most of them will meet the background noise problem when trying to find more discriminative regions. In this paper, we address it in a knowledge transfer learning manner. Multiple models are trained one by one, and all previously trained models are regarded as teacher models to supervise the training of the current one. Specifically, a orthogonal loss (OR-loss) is proposed to encourage the network to find diverse and meaningful regions. In addition, the first model is trained with only CE-Loss. Finally, all models' outputs with complementary knowledge are combined together for the final prediction result. We demonstrate the superiority of the proposed method and obtain state-of-the-art (SOTA) performances on three popular FGVC datasets. © 2021 IEEE

关键词： Distillation

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Clarity in chaos: Boosting few-shot classification through information suppression and sparsification

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pattern recognition 2025年 167卷

作者： Li, Xiaoxu Ji, Luchen Zhu, Rui Ma, Zhanyu Xue, Jing-Hao School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China Faculty of Actuarial Science and Insurance Bayes Business School City St George's University of London LondonEC1Y 8TZ United Kingdom Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Statistical Science University College London LondonWC1E 6BT United Kingdom

The advance of deep learning has invigorated the research of few-shot classification. However, the interference of non-target information in feature representations hampers classification generalization. To tackle this issue, we propose an irrelevant information suppression (IIS) module, which is focused on suppressing the weight of unimportant information and elevating the sparsity of feature representations. An IIS network with three consecutive IIS modules is developed, to illustrate the progressive suppression of unimportant information and highlighting of key discriminative features of the target. Extensive experiments showcase the superior performance of our IIS network on five widely-used benchmark datasets. Furthermore, we show that the IIS module can be readily used as a plug-in module by state-of-the-art few-shot classifiers, and can clearly further improve their performance. Our code is available on GitHub at https://***/LC4188/IISNet. © 2025

关键词： Taxonomies

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Research on a novel restoration algorithm of turbulence-degraded images with alternant iterations

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Journal of systems Engineering and Electronics 2006年第3期17卷 477-482页

作者： Liu Chunsheng Hong Hanyu Zhang Tianxu Inst. for Pattern Recognition and AI State Key Laboratory of Image Processing and Intelligent Control Huazhong Univ. of Science and Technology Wuhan 430074 P. R. China

A new restoration algorithm based on double loops and alternant iterations is proposed to restore the object image effectively from a few frames of turbulence-degraded images, Based on the double loops, the iterative relations for estimating the turbulent point spread function PSF and object image alternately are derived. The restoration experiments have been made on computers, showing that the proposed algorithm can obtain the optimal estimations of the object and the point spread function, with the feasibility and practicality of the proposed algorithm being convincing.

关键词： turbulence-degraded image image restoration double loops alternant iterations.

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Size and Defect Detection of Valve Based on Computer Vision

Size and Defect Detection of Valve Based on Computer Vision

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2022 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2022

作者： Yan, Xiaobo Feng, Le Tan, Mian Yang, Yuan Zhang, Yu Wang, Lin Guizhou Key Laboratory of Pattern Recognition and Intelligent System China Guizhou Minzu University Guiyang550025 China Guiyang Fusheng Intelligent Technology Co. Ltd China

ISBN: (纸本)9781510663350

Engine valve is the core component of the engine, and its quality determines the performance of the engine. In industrial production quality inspection, it is necessary to detect the size of the valve and whether there are defects on the surface. Usually, the quality of the valve is determined by comparing the image of the valve surface with the standard image. However, the existing surface defect detection technology cannot detect the curved surface device. In order to solve this problem, this paper designs a valve size and defect detection method based on computer vision. The experimental results show that the method can quickly and accurately detect the rod diameter, groove radius and surface defects of the valve. The method is practical, robust and real-time. © 2023 SPIE.

关键词： Computer vision

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Towards Interpretable Defense Against Adversarial Attacks via Causal Inference

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Machine Intelligence Research 2022年第3期19卷 209-226页

作者： Min Ren Yun-Long Wang Zhao-Feng He University of Chinese Academy of Sciences Beijing 100190China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China Laboratory of Visual Computing and Intelligent System Beijing University of Posts and TelecommunicationsBeijing 100876China

Deep learning-based models are vulnerable to adversarial attacks. Defense against adversarial attacks is essential for sensitive and safety-critical scenarios. However, deep learning methods still lack effective and efficient defense mechanisms against adversarial attacks. Most of the existing methods are just stopgaps for specific adversarial samples. The main obstacle is that how adversarial samples fool the deep learning models is still unclear. The underlying working mechanism of adversarial samples has not been well explored, and it is the bottleneck of adversarial attack defense. In this paper, we build a causal model to interpret the generation and performance of adversarial samples. The self-attention/transformer is adopted as a powerful tool in this causal model. Compared to existing methods, causality enables us to analyze adversarial samples more naturally and intrinsically. Based on this causal model, the working mechanism of adversarial samples is revealed, and instructive analysis is provided. Then, we propose simple and effective adversarial sample detection and recognition methods according to the revealed working mechanism. The causal insights enable us to detect and recognize adversarial samples without any extra model or training. Extensive experiments are conducted to demonstrate the effectiveness of the proposed methods. Our methods outperform the state-of-the-art defense methods under various adversarial attacks.

关键词： Adversarial sample adversarial defense causal inference interpretable machine learning transformers

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