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2nd Sino-Foreign-Interchange Workshop on Intelligence Science and intelligent Data Engineering (IScIDE)

作者： Hou, Biao Cheng, Xi Jiang, Hua Qiong Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China China

ISBN: (纸本)9783642319181

The compressive sensing theory shows that signals and images can be recovered from far fewer samples than that used in Shannon sampling theorem. In practical applications our aim is that an object should be meaningfully reconstructed in considerable detail relative to the full scene elsewhere at low sampling rate so that the target will be found directly from the reconstructed image. To achieve the goal, a new method based on manifold-based compressive sensing is proposed for object specific image reconstruction, in which the whole image is divided into small pieces and reconstructed piece by piece with the probability density function of the target as prior knowledge. Our reconstruction method is very fast since we divided the whole image into small pieces and the small pieces are reconstructed respectively. In our method, modeling a manifold of a target and getting the probability density function of the target is the key issue. And the model, which is used to obtain the probability density function about the target, is a Mixture of Factor Analyzers (MFA). The experiments results show that the target can be reconstructed more clearly than the full scene elsewhere at the low sampling rate with our method.

关键词： Manifold-based Compressive sensing Object MFA Prior knowledge

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Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling

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中国物理快报（英文版） 2009年第6期26卷 59-62页

作者： CHEN Jian-Rui JIAO Li-Cheng WU Jian-She WANG Xiao-Hua Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligence Information ProcessingXidian UniversityXi'an 710071 College of Science Inner Mongolia University of TechnologyInner Mongolia 01 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligence Information ProcessingXidian UniversityXi'an 710071

A new general network model for two complex networks with time-varying delay coupling is *** we investigate its synchronization *** two complex networks of the model differ in dynamic nodes,the number of nodes and the coupling *** using adaptive controllers,a synchronization criterion is *** examples are given to demonstrate the effectiveness of the obtained synchronization *** study may widen the application range of synchronization,such as in chaotic secure communication.

关键词： No English keywords

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Computational Intelligence in Remote Sensing image Registration:A survey

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International Journal of Automation and computing 2021年第1期18卷 1-17页

作者： Yue Wu Jun-Wei Liu Chen-Zhuo Zhu Zhuang-Fei Bai Qi-Guang Miao Wen-Ping Ma Mao-Guo Gong Key Laboratory of Big Data and Intelligent Vision School of Computer Science and TechnologyXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic EngineeringXidian UniversityXi′an 710071China

In recent years,computational intelligence has been widely used in many fields and achieved remarkable *** computing and deep learning are important branches of computational *** methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image *** paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep *** the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is *** application of deep learning in remote sensing image registration is also *** the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.

关键词： Computational intelligence evolutionary computation neural network deep learning remote sensing image registration

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LGSNet: Local-Global Semantics Learning Object Detection

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IEEE Transactions on Multimedia 2024年 27卷 2281-2292页

作者： Li, Yang Jiao, Licheng Liu, Xu Liu, Fang Li, Lingling Chen, Puhua Xidian University Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China International Research Center of Intelligent Perception and Computation School of Artificial Intelligence Xi'an China

Self-attention learns capturing the long-range dependencies between token embeddings (e.g., image pixels). However, the memory overhead and computation cost are prohibitive due to being quadratic in term of the spatial resolution. The structure analysis is conducted on the vanilla attention and reveals two crucial roles: the correlation-based dependency structure and feature normalization. In this work, an efficacious Local-Global Semantics (LGS) module is proposed to alleviate the above issues. Our LGS module contains a group convolution and an Efficient Global Semantic Attention (EGSA). Firstly, the group convolution aggregates local semantics. Secondly, EGSA formulates a general model for the global semantic interaction. The feature normalization is directly applied to sequence representations (e.g., query, key or value) and the linear semantic correlation is calculated between channels. LGS has the linear memory overhead and computation cost in term of the spatial resolution. The proposed LGS module can be seamlessly incorporated into a backbone network. The experiment results verify its effectiveness on two popular detection datasets: the MS COCO and PASCAL VOC. The codes will be given in https://***/TimeIsFuture/LGS. © 1999-2012 IEEE.

关键词： Semantics

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Immunodominance and clonal selection inspired multiobjective clustering

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Progress in Natural Science:Materials International 2009年第6期19卷 751-758页

作者： Wenping Ma , Licheng Jiao, Maoguo Gong key laboratory of intelligent perception and image understanding of the Ministry of Education of China, Institute of intelligent Information Processing, Xidian University, Xi’an 710071, China [a]Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.

关键词： Artificial immune systems Multiobjective optimization Clustering Unsupervised learning

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Concatenated edge and co-occurrence feature extracted from Curvelet Transform for human detection

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Journal of Information and Computational Science 2011年第4期8卷 662-670页

作者： Han, Hong Fan, Youjian Jiao, L.C. Chen, Zhichao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

An efficient feature extraction method based on the Curvelet Transform for detecting human in static images is proposed in this paper. The edge features can be extracted with the block-based statistical information of each sub-band coefficients, and then the texture feature can be extracted from the co-occurrence matrixes of the lowest-frequency sub-band coefficients, all the extracted features are concatenated as the final feature vector of the images. All the training and test data are from the INRIA and MIT human dataset. The classification results with test data show that the proposed feature extraction method is suitable for human detection. From the detection results, it can be seen that, the detection accuracy of the proposed method is higher, meanwhile the false positive rate is lower than the Histograms of Oriented Gradients (HOG) method. Copyright © 2011 Binary Information Press.

关键词： Feature extraction

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A novel quantum-inspired immune clonal algorithm with the evolutionary game approach

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Progress in Natural Science:Materials International 2009年第10期19卷 1341-1347页

作者： Qiuyi Wu,Licheng Jiao,Yangyang Li,Xiaozheng Deng key laboratory of intelligent perception and image understanding of Ministry of Education of China,Institute of intelligent Information Processing,Xidian University,Xi’an 710071,China [a]Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve combination optimization *** this paper,we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary *** dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this *** results indicate that the proposed approach maintains a good diversity and achieves superior performance.

关键词： Game theory Evolutionary game Replicator dynamics Quantum-inspired optimization Artificial immune system

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Multiscale Representation Learning for image Classification: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 23-43页

作者： Jiao, Licheng Gao, Jie Liu, Xu Liu, Fang Yang, Shuyuan Hou, Biao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi'an710071 China

Feature representation has been widely used and developed recently. Multiscale features have led to remarkable breakthroughs for representation learning process in many computer vision tasks. This paper aims to provide a comprehensive survey of the recent multiscale representation learning achievements in classification tasks. Multiscale representation learning methods can be divided into two broad categories (multiscale geometric analysis and multiscale networks). Eleven kinds of multiscale geometric tools and seven kinds of multiscale networks are introduced. Some corresponding fundamental subproblems of these two broad categories are also described, including some concepts in representation process, specific representation methods with multiscale geometric analysis, and multiscale representation design strategies for networks. Then, the correlation between these two broad categories is illustrated, including respective characteristics, combination strategies, and characteristics of optimal representation. Some datasets and evaluation results are included to verify the effectiveness of the multiscale representation learning. Eventually, conclusion and future work are given, covering four directions [a) Choice and fusion;b) Self-adaption;c) Structure;and d) Generalization and proof]. © 2022 IEEE.

关键词： Deep neural networks

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image segmentation based on differential immune clone clustering algorithm

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International Journal of intelligent Computing and Cybernetics 2013年第1期6卷 83-102页

作者： Wenping Ma Feifei Ti Congling Li Licheng Jiao Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian UniversityXi’anChina

Purpose–The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm(DICCA)to solve image ***/methodology/approach–DICCA combines immune clone selection and differential evolution,and two populations are used in the evolutionary *** reproduction and selection,differential mutation,crossover and selection are adopted to evolve two populations,which can increase population diversity and avoid local *** extracting the texture features of an image and encoding them with real numbers,DICCA is used to partition these features,and the final segmentation result is ***–This approach is applied to segment all sorts of images into homogeneous regions,including artificial synthetic texture images,natural images and remote sensing images,and the experimental results show the effectiveness of the proposed ***/value–The method presented in this paper represents a new approach to solving clustering *** novel method applies the idea two populations are used in the evolutionary *** proposed clustering algorithm is shown to be effective in solving image segmentation.

关键词： Differential evolution Clone selection Clustering image segmentation image processing Cluster analysis

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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