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Support vector machine with mixture of kernels for automatic image annotation

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High Technology Letters 2013年第3期19卷 295-300页

作者：田东平 Zhao Xiaofei shi zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic *** this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is *** one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as *** the other hand,SVM-MK is constructed to shoot for better annotating *** results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation.

关键词： automatic image annotation ( AIA) support vector machine ( SVM) kernel func-tion principal component analysis (PCA)

Fusing PLSA model and Markov random fields for automatic image annotation

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High Technology Letters 2014年第4期20卷 409-414页

作者：田东平 Zhao Xiaofei shi zhongzhi Institute of Computer Software Baoji University of Arts and Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the *** novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image *** demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

关键词： automatic image annotation probabilistic latent semantic analysis (PLSA) expectation maximization Markov random fields (MRF) image retrieval

A visual awareness pathway in cognitive model ABGP

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High Technology Letters 2016年第4期22卷 395-403页

作者：马刚 Yang Xi Lu Chengxiang Zhang Bo shi zhongzhi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 P. R. China University of Chinese Academy of Sciences Beijing 100190 P. R. China

The cognitive model ABGP is a special model for agents,which consists of awareness,beliefs,goals and plans. The ABGP agents obtain the knowledge directly from the natural scenes only through some single preestablished rules like most agent architectures. Inspired by the biological visual cortex( V1) and the higher brain areas perceiving the visual feature,deep convolution neural networks( CNN) are introduced as a visual pathway into ABGP to build a novel visual awareness module. Then a rat-robot maze search simulation platform is constructed to validate that CNN can be used for the awareness module of ABGP. According to the simulation results,the rat-robot implemented by the ABGP with the CNN awareness module reaches the excellent performance of recognizing guideposts,which directly enhances the capability of the communication between the agent and the natural scenes and improves the ability to recognize the real world,which successfully demonstrates that an agent can independently plan its path in terms of the natural scenes.

关键词： ABGP visual cortex(V1) convolution neural networks(CNN) awareness visual pathway

A dynamic description logic based system for video event detection

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Frontiers of Electrical and Electronic Engineering in China 2010年第2期5卷 137-142页

作者： Xiaofeng WANG Liang CHANG Zhixin LI zhongzhi shi The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Graduate University of Chinese Academy of Sciences Beijing 100049China School of Computer and Control Guilin University of Electronic TechnologyGuilin 541004China

Video event detection is an important research area *** the video event is a key problem in video event *** this paper,we combine dynamic description logic with linear time temporal logic to build a logic system for video event *** proposed logic system is named as LTD_(ALCO)which can represent and inference the static,dynamic and temporal knowledge in one uniform logic *** on the LTD_(ALCO),a framework for video event detection is *** video event detection framework can automatically obtain the logic description of video content with the help of ontology-based computer vision techniques and detect the specified video event based on satisfiability checking on LTD_(ALCO)formulas.

关键词： video event semantics dynamic description logics reasoning ontology

Foundations of Intelligence Science

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International Journal of Intelligence Science 2011年第1期1卷 8-16页

作者： zhongzhi shi 不详

In order to make significant progress toward achievement of human level machine intelligence a paradigm shift is needed. More specifically, the natural intelligence and artificial intelligence should be closely interacted in Intelligence Science study, instead of separate from each other. In order to reach the paradigm, brain science, cognitive science, artificial intelligence and others should cross-research together. Brain science explores the essence of brain, research on the principle and model of natural intelligence in molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. Artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. All together pursue to explore the mechanism and principle of intelligence which is the engine of advanced science and technology. The paper will give the definition of intelligence and discuss ten big issues of Intelligence Science. The conclusion and perspective will be given in last section.

关键词： Intelligence Intelligence Science Machine Intelligence

Cognitive Machine Learning

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International Journal of Intelligence Science 2019年第4期9卷 111-121页

作者： zhongzhi shi Institute of Computing Technology Chinese Academy of Sciences Beijing China

Cognitive machine learning refers to the combination of machine learning and brain cognitive mechanism, specifically, combining machine learning with mind model CAM. Three research directions are proposed in this pape... 详细信息

关键词： Cognitive Machine Learning Emergency of Learning Complementary Learning System Evolution of Learning Intelligence Science Mind Model CAM

Inductive transfer learning for unlabeled target-domain via hybrid regularization

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Chinese Science Bulletin 2009年第11期54卷 2470-2478页

作者： ZHUANG FuZhen LUO Ping HE Qing shi zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Hewlett Packard Labs China Beijing 100084 China Graduate University of Chinese Academy of Sciences Beijing 100190 China

Recent years have witnessed an increasing interest in transfer learning. This paper deals with the classification problem that the target-domain with a different distribution from the source-domain is totally unlabeled, and aims to build an inductive model for unseen data. Firstly, we analyze the problem of class ratio drift in the previous work of transductive transfer learning, and propose to use a normalization method to move towards the desired class ratio. Furthermore, we develop a hybrid regularization framework for inductive transfer learning. It considers three factors, including the distribution geometry of the target-domain by manifold regularization, the entropy value of prediction probability by entropy regularization, and the class prior by expectation regularization. This framework is used to adapt the inductive model learnt from the source-domain to the target-domain. Finally, the experiments on the real-world text data show the effectiveness of our inductive method of transfer learning. Meanwhile, it can handle unseen test points.

关键词：归纳学习正规化标签杂交归一化法预测概率文字资料归纳法

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Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

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Intelligent Automation & Soft Computing 2023年第9期37卷 2783-2807页

作者： Dongping Tian Bingchun Li Jing Liu Chen Liu Ling Yuan zhongzhi shi School of Computer Science and Technology Kashi UniversityKashi844006China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China

Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence ***,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal *** this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the ***,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution *** be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution *** by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm *** addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO ***,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

关键词： Particle swarm optimization local optima acceleration coefficients swarm diversity premature convergence

Analysis and Applications of PCA Information Features

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通讯和计算机（中英文版） 2005年第9期2卷 25-31页

作者： shifei Ding zhongzhi shi College of lnformation Science and Engineering Shandong Agricultural University Tai ＇an 271018 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beo＇ing 100080 China