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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1728-1742页

作者： Zhao, Changming Peng, Ruimin Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 China and alsowith the ShenzhenHuazhong University of Science and Technology Research Institute Shenzhen 518029 China

Ensemble learning aggregates outputs from multiple base learners for better performance. Bootstrap aggregating (bagging) and boosting are two popular such approaches. They are suitable for integrating unstable base learners with large variance and weak base learners with large bias, respectively, but not base learners with small variance and/or bias, e.g., support vector machine, regularized logistic regression, and ridge regression. This article proposes two novel ensemble-learning-based fine-tuning approaches, boosting fine-tuning (BF) and bagging and boosting fine-tuning (BBF), to fine-tune learners with small variance and/or bias for better performance. BF embeds boosting in a single hidden layer neural network. In each iteration, BF first uses the Newton's method to generate a temporary training set, and then trains a boosting learner on it. BBF combines BF and bagging. It first uses bootstrap to obtain multiple replicas of the training set, and then trains a BF learner on each replica. Extensive experiments on 46 real-world datasets demonstrated that BBF is flexible, robust, and effective, and can fine-tune many popular classifiers to achieve better generalization performance. © 2020 IEEE.

关键词： Support vector machines

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ICSA based projection pursuit clustering with LDA index and its application in SAR image segmentation

ICSA based projection pursuit clustering with LDA index and ...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Gou, Shuiping Feng, Jing Jiao, Licheng He, Xiao Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

The performance of the traditional clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, Projection Pursuit with LDA index based on Immune Clonal Selection Algorithm is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimal projection direction. In the proposed algorithm, k-mean clustering algorithm is used to partition the reduced data and give labels to LDA index, and iterations have been done for several times can converge quickly to obtain a clustering result. The experimental results on UCI data show that the present method has better clustering accuracy compared with traditional linear dimension reduction method for principle component analysis (PCA). And the experiments on SAR image segmentation also have a good performance. ©2009 IEEE.

关键词： Clustering algorithms

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3D ultrasound data acqusition system based on back end scan mode

3D ultrasound data acqusition system based on back end scan ...

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2011 IEEE International conference on intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Qi, Jian Ding, Mingyue Yuchi, Ming Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Life Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769546230

The data acquisition of 3D-Ultrasound includes array scan and mechanical scan, and the later one is more easy to realize. Currently, the traditional probe scanning mode is Front-end scanning. Under the above scanning mode, when it scans over the breast, 2D-Ultrasound probe slides through the surface of the patients' bodies, the image will be influenced strongly by the human *** this paper we propose a new scanning mode to solve the above problem: Back-End scan, the back end rotates while the front end contacts the patients' skin without slide. The device designed using dual stepper motors which are under the synchronization control. Experiment results show the effectiveness of the proposed device. © 2011 IEEE.

关键词： Scanning

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SAR image segmentation based on immune greedy spectral clustering

SAR image segmentation based on immune greedy spectral clust...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Gou, S.P. Zhang, J. Jiao, L.C. Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In this paper we propose a novel spectral clustering algorithm called Immune Greedy Spectral Clustering Algorithm, which introduces immune clone selection algorithm instead of greedy selection to choose a subset before greedy spectral embedding without any prior experiment knowledge. As we know, Nyström algorithm is a random selecting method which depends so much on the selecting result so that the clustering result fluctuates obviously. Greedy spectral clustering algorithm acquires a subset called dictionary by greedy selection algorithm so that the result could be more stable and better. However, to choose an appropriate input tolerance, we need prior experiment knowledge about the relationship between tolerance and select number. Moreover, since the criterion used for greedy selection is the distance in feature space between a candidate example and its projection on the subspace spanned by selected examples, we need to compute every example point one by one to get the error of using the selected examples to approximate the candidate example and then decide whether to choose it. So the time expense increases inevitable. Considering all above, we present a new method called Immune Greedy Spectral Clustering Algorithm. The experimental results show that Immune Greedy Spectral Clustering Algorithm need no prior experiment knowledge and could save time compared with greedy spectral clustering while getting a better accuracy rate compared with Nyström algorithm. ©2009 IEEE.

关键词： Cloning

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Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

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Science in China(Series F) 2008年第8期51卷 1064-1082页

作者： GONG MaoGuo JIAO LiCheng MA WenPing DU HaiFeng 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 School of Public and Administration Xi'an Jiaotong University Xi'an 710049 China

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.

关键词： multiobjective optimization immunodominance clonal selection artificial immune systems evolutionaryalgorithms

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Transfer learning for kernel matching pursuit on computer-aided diagnoses

Transfer learning for kernel matching pursuit on computer-ai...

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MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Gou, Shuiping Yao, Yao Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478085

It is commonplace for the lack of labeled data in novel domains on medical image computer-aided diagnosis but there have been some labeled data or prior knowledge in old correlative domains. In this paper, instance-transfer approach is introduced into medical image processing. And then we present a novel transfer learning model based on kernel matching pursuit called TLKMP, which extends KMP (kernel matching pursuit learning machine, Vincent & Bengio, 2002). TLKMP uses the Greedy Approximation Residue to transfer instances into target domains which have little labeled set different distributions from the source domains. So, valuable instances in resource domains are reused to construct high quality classification model for the unlabeled set of the target domains. The experiment is performed datasets on Gastric Cancer of Lymph Node database which comes from some a hospital. The results show that the proposed algorithm has better classification performance compared with traditional KMP methods, and it improves diagnosis accuracy rate of medical images effectively the same as the algorithm need lest labeled data for training a good classification model. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Computer aided diagnosis

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Blind noisy image separation based on a new robust independent component analysis network

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Chinese Optics Letters 2006年第10期4卷 573-575页

作者：纪建田铮 Department of Computer Science & Technology Northwestern Polytechnical University Xi'an 710072 Department of Applied Mathematics Northwestern Polytechnical UniversityXi'an 710072 Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & TechnologyWuhan 430074

The separation of noisy image is a very exciting area of research, especially when no prior information is available about the noisy image. In this paper, we propose a robust independent component analysis （ICA） network for separation images contaminated with high-level additive noise or outliers. We reduce the power of additive noise by adding outlier rejection rule in ICA. Extensive computer simulations confirm robustness and the excellent performance of the resulting algorithms.

关键词： Algorithms Computer simulation Independent component analysis

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Transfer network learning based remote sensing target recognition

Transfer network learning based remote sensing target recogn...

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Gou, Shuiping Wang, Yuqin Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

The target recognition accuracy of remote sensing images is not satisfied. The labels of images acquisition and recollecting are difficult and expensive. In order to solve the problem, we introduce transfer learning into Network Boosting algorithm (NB) and propose Transfer Network Learning algorithm (TNL), in which other out-date data are reused to instruct the remote sensing target recognition. TNL is suitable to improve the performance of remote sensing target recognition, in which instances transfer learning is adopted for domain adaptation. The experimental results on the MSTAR SAR data set and remote sensing data set including two-class planes show that the proposed algorithm has better performance and achieves different domains learning. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Learning algorithms

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Contour Extraction via Contextual Modulation

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电子学报(英文版) 2009年第2期18卷 329-331页

作者： HUANG Wentao JIAO Licheng XU Yuelei SUN Ye JIA Jianhua Institute of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi＇an 710071 China Department of Avionics Engineering Engineering College of Air Force Engineering University Xi＇an 710038 China

Contour extraction is an important task in image processing and computer vision. The contextual modulation is a universal phenomenon in the primary visual cortex (VI). A biologically motivated computational model is presented for contour extraction in this paper. Two mechanisms of contextual modulation, surround suppression and collinear facilitation, are integrated in this model. We obtain good results via this model to extract contours from images with noise and texture backgrounds. This work provides a biologically motivated approach with great potential for computer vision.

关键词：轮廓提取调制语境动力计算模型计算机视觉初级视觉皮层图像处理图像轮廓

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Low bit-rate SAR image encoding using bandelets with adaptive quadtree partition

Low bit-rate SAR image encoding using bandelets with adaptiv...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Yang, Shuyuan Meng, Huixiao Wu, Ruixia Jiao, Licheng Key Laboratory of Intelligent Perception Image Understanding of Ministry of Education Institute of Intelligence Information Processing Xi'an 710071 China

ISBN: (纸本)9781424427314

Synthetic aperture radar (SAR) is an imaging system which provides high-resolution images of earth surface. Nowadays there is an ever-growing interest in the SAR data compression because of the huge resources which require for storage and transmission. In this paper, we address the problem of SAR image compression and apply the multiscale Bandelet with adaptive quadtree partition to solve it. Because Bandelet can provide an efficient representation of geometric structures existed in images, the Bandelet based method can capture abundant textures in SAR images. Moreover, the multiscale Bandelet with adaptive quadtree partition can approximate any piecewise Cα-regular functions when α is unknown, so Bandelet can represent the hierarchy textures of SAR images efficiently. Embedded Block Coding with Optimal Truncation (EBCOT) coding algorithm is employed in our proposed method. Experimental results show that our method outperforms JPEG2000 and the second generation Bandelet (2G-Bandelet) compression methods on SAR image encoding at low bit rates. ©2009 IEEE.

关键词： Synthetic aperture radar

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