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Center matching scheme for K-means cluster ensembles

Center matching scheme for K-means cluster ensembles

MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Wu, Caili Huo, Jieting Zou, Haishuang Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster ensemble learning. Each k-means clusterer outputs a sequence with k cluster centers. We randomly select a cluster center sequence as a reference one, and then we rearrange the other cluster center sequences according to the reference sequence. Then we label the data using these matched cluster center sequences. Hence we get multiple partitions or clusterings. Finally, multiple clusterings are combined to the best labeling by using combination rules, such as the majority voting rule, the weighted voting rule and the selective weighted voting rule. Experimental results on 7 UCI data sets show that our ensemble methods could improve the clustering results effectively. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Clustering algorithms

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Layer-aware forward error correction for mobile broadcast of layered media

Layer-aware forward error correction for mobile broadcast of...

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作者： Hellge, Cornelius Gómez-Barquero, David Schierl, Thomas Wiegand, Thomas Multimedia Communications Group Image Processing Department Fraunhofer HHI 10587 Berlin Germany Image Communication Laboratory Berlin Institute of Technology 10623 Berlin Germany Universidad Politécnica de Valencia 46022 Valencia Spain Image Processing Department Fraunhofer HHI 10587 Berlin Germany

The bitstream structure of layered media formats such as scalable video coding (SVC) or multiview video coding (MVC) opens up new opportunities for their distribution in Mobile TV services. Features like graceful degradation or the support of the 3-D experience in a backwards-compatible way are enabled. The reason is that parts of the media stream are more important than others with each part itself providing a useful media representation. Typically, the decoding of some parts of the bitstream is only possible, if the corresponding more important parts are correctly received. Hence, unequal error protection (UEP) can be applied protecting important parts of the bitstream more strongly than others. Mobile broadcast systems typically apply forward error correction (FEC) on upper layers to cope with transmission errors, which the physical layer FEC cannot correct. Today's FEC solutions are optimized to transmit single layer video. The exploitation of the dependencies in layered media codecs for UEP using FEC is the subject of this paper. The presented scheme, which is called layer-aware FEC (LA-FEC), incorporates the dependencies of the layered video codec into the FEC code construction. A combinatorial analysis is derived to show the potential theoretical gain in terms of FEC decoding probability and video quality. Furthermore, the implementation of LA-FEC as an extension of the Raptor FEC and the related signaling are described. The performance of layer-aware Raptor code with SVC is shown by experimental results in a DVB-H environment showing significant improvements achieved by LA-FEC. © 2011 IEEE.

关键词： Scalable video coding

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Joint Generic Learning and Multi-source Domain Adaptation on Unsupervised Face Recognition 1

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Xu, Zhi Han, Han Ji, Jianqiu Qiang, Baohua Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

For most machine learning algorithms, the performance of model trained on the source domain degenerates obviously when it is applied to the target domain because the distributions of both domains are different. Besides, in many cases, there is a lack of adequate supervised target domain training samples. In order to solve the mismatch between the domains and the lack of training samples, this paper proposes jointing domain adaptation and generic learning on solving unsupervised face recognition. Firstly, samples are selected randomly from multiple source and target domains which do not contain interest subjects and construct multiple sub-datasets. Secondly, learning a common subspace for each sub-dataset. In the common subspace, source and target domains can mutual interlace and their structures are well preserved, and we can get more discrimination information from multiple feature subspaces. Finally, the recognition is obtained by using combine strategies. The experimental results show that the recognition performance of the framework is better than that of the competitive ones. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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A Novel Method to Get Super-Resolution images from Low-Resolution Compressed Video

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The Journal of China Universities of Posts and Telecommunications 2005年第2期12卷 50-54页

作者： ZHOU Liang ZHU Xiu-chang Information Industry Ministry and Jiangsu Province Key Lab of Image Processing and Image Communication Nanjing University of Posts and Communications Nanjing 210003 P. R. China

In order to resolve the problems of discontented restoration effect and confined applying scope which exist in the current compressed video restoration algorithms, a novel method to get super-resolution images from low-resolution compressed video is proposed in this paper. At first, a uniform model is presented and the restoration problem in the Bayesian framework is formulated under the MAP criterion, then the focus is put on the hybrid motion-compensation and transform coding schemes, at last the methods of getting the parameters are provided. The results of the simulation clearly demonstrate that our method not only has the properties of finer vision effect and wider applying scope, but also performs better than those of current classical algorithms in the aspects of Peak Signal Noise Ratio （PSNR） under the basis of the same condition.

关键词： super-resolutlon compressed video restoration

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Wideband and Replaceable Cylindrical Dielectric Resonator Antennas

Wideband and Replaceable Cylindrical Dielectric Resonator An...

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2020 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2020

作者： Weng, Ling Peng Fang, Xiao Sheng Yan, Jing Shi, Kang Ping Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Department of Electronic and Information Engineering Shantou China

ISBN: (纸本)9781728160641

A wideband and replaceable dielectric resonant antenna (DRA) excited by a probe is investigated. The proposed wideband DRA consists of a probe platform and a solid cylindrical dielectric resonator, and two magnets are applied to fix them. In this way, the dielectric resonator can be easily replaced. Therefore, three kinds of wideband DRAs with impedance bandwidth of 35.8%, 56.6% and 74% are designed. The reflection coefficient, radiation mode and antenna gain of the cylindrical DRA were analyzed by using HFSS ANSYS. © 2020 IEEE.

关键词： Microwave antennas

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Research summary of "Camellia oleifera fruit picking robot" technical analysis

Research summary of "Camellia oleifera fruit picking robot" ...

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2021 International Conference on Signal processing and communication Technology, SPCT 2021

作者： Wang, Liang Hou, Yifeng Wang, Kuikui Wei, Chunming Guangxi Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou Guangxi543003 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

Aiming at the problems of my country's current camellia fruit picking mainly relying on manual picking, large labor and low efficiency, the development and promotion of camellia fruit picking equipment and technology are discussed, and technical analysis is carried out for the design of camellia fruit picking robots. This article analyzes the characteristics and existing problems of camellia fruit picking, and analyzes the four major components of the picking robot: "end effector", "manipulator motion control system", "visual recognition positioning system"and "driving walking device". In summary, summarize the problems that have been solved, the results achieved, and the difficulties that still exist, so as to provide enlightenment for the subsequent exploration of the subject research of "camellia fruit picking robot". © 2022 SPIE.

关键词： Fruits

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Few-Shot Learning for image Classification 1

Few-Shot Learning for Image Classification

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Chen, Peining Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intellig ent Processing Guilin China

ISBN: (纸本)9798331504434

In certain specialized domain scenarios, the collection of image data is rendered time-consuming and costly due to a variety of unique issues, making the assembly of extensive image datasets a challenging endeavor. When compared to the thousands, if not tens of thousands of image data required for deep learning models, the amount of image data that can be procured is significantly dwarfed. However, few-shot learning facilitates the training of models with a limited number of labeled images, eliminating the need for extensive data resources. In this paper, image enhancement activated to enrich the finite image dataset, thereby diversifying the data and highlighting key features through the improvement of visual effects and machine interpretability. Additionally, the method of transfer learning is engaged to optimize the classification performance of deep learning models. The selection of the ResNet-50 architecture serves as the foundational framework for the network. Prior knowledge is obtained from the imageNet dataset. A strategy that combines image enhancement and transfer learning, where the original data is processed using image enhancement to improve the convergence rate of the loss function and the model's classification accuracy. © 2024 IEEE.

关键词： Zero-shot learning

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Pre-extracting boundary vectors for support vector machine using pseudo-density estimation method

Pre-extracting boundary vectors for support vector machine u...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Chen, Guirong Zhou, Hongjie Ye, Ning Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

Support vector machines (SVMs) have become useful and universal learning machines. SVMs construct a decision function by support vectors (SVs) and their corresponding weights. The training phase of SVMs definitely uses all training samples, which leads to a large computational complexity for a large scale sample set. Moreover support vectors could not be found until a quadratic programming (QP) problem is solved. Actually we know only SVs play a role in the decision function. Hence, pseudo density estimation (PDE) is presented to extract a set of boundary vectors (BVs) which may contain SVs. The PDE method is a variant of Parzen window method. Hyperspheres are considered as the window functions. In our method, for each sample we construct a hypersphere with an unfixed radius. The ratio of the number of samples contained in the hypersphere of a sample to the total training samples can be taken as the pseudo density of the corresponding sample. The set of BVs is taken as the training input to SVMs. In doing so, it speeds the training procedure of SVMs. It is convenient for PDE to determine its parameter. The experiments show that SVMs using PDE have the similar generalization performance to SVMs. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Support vector machines

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Depth and Width Adaption of DNN for Data Stream Classification with Concept Drifts 24th

Depth and Width Adaption of DNN for Data Stream Classificati...

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24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Zhou, XingZhi Liu, Xiang Wen, YiMin Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031482311

To handle data stream classification with concept drifts, recent studies have shown that a continuously evolving network structure can achieve better performance. However, firstly, they only change one hidden node at a time, which is not enough to alleviate underfitting or overfitting of network model, leading to model’s inability to fit data well. Secondly, during the growth process of the network, they did not consider reducing hidden layers, which would affect the learning ability of deep neural network models. To overcome these shortcomings, an adaptive neural network structure (ANSN) is proposed to handle data stream classification with concept drifts. ANSN has a completely open structure, its network structure, depth, and width can evolve automatically in online mode. The experimental results on ten popular data stream datasets show that the proposed ANSN outperforms the comparison methods. The codes of the proposed algorithm is available on https://***/ymw12345/***. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deep neural networks

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GFENet: Enabling Attention and Adaptive Feature Fusion in Real-time Object Detection 2

GFENet: Enabling Attention and Adaptive Feature Fusion in Re...

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2nd International Symposium on Electrical, Electronics and Information Engineering, ISEEIE 2022

作者： Pan, Chaolin Zhu, Xiaoyang Chu, Jun Nanchang Hangkong University Department of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang China

ISBN: (数字)9781665468749

ISBN: (纸本)9781665468749

Real-time object detection has been a critical component of onboard instrumentation for next-generation autonomous driving. To enable safety and reliability in autonomous driving systems, we must continue advancing its real-time object detection accuracy for future autonomous driving systems in complex environments. Existing works in real-time object detection cannot accurately detect objects with similar appearances and in complex environments. In particular, existing approaches missed many essential features because of the loss of multi-scale contextual features. Therefore, they cannot differentiate objects that have a similar appearance, a low-resolution, and a small size. To solve these issues, and to advance real-time object detection accuracy, we develop GFENet (Global Feature Enhancement Network), an end-to-end real-time object detection framework. First, we introduce a convolutional attention module that integrates spatial and channel-wise features and eliminates ineffective features to enable a strong feature representation of the network, followed by a lightweight residual module that further advances the feature representation by highlighting salient changes in features. Second, we build an adaptive feature fusion module to adaptively learn weights of multi-scale contextual features across layers, aiming to strengthen weights for small-size objects. We comprehensively evaluate GFENet on two leading object detection datasets-PASCAL VOC and MS COCO. The evaluation results (mAP of 82.9 and FPS of 31 on PASCAL VOC;mAP of 39.0 and FPS of 23 on MS COCO) show the outperformance of GFENet compared to competing baseline methods. © 2022 IEEE.

关键词： Instruments Object detection Detectors Feature extraction Real-time systems Safety Reliability

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