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检索条件"机构=State Key Laboratory of Intelligent Technology& Systems"

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Semantic Concept Learning through Massive Internet Video Mining

Semantic Concept Learning through Massive Internet Video Min...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Peijiang Yuan Bo Zhang Jianmin Li State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

Semantic concept learning is one of the most challenging problems in video retrieval. The key barrier for semantic concept learning is lack of annotated training data. Internet videos are different from ordinary videos: massive, rich information, customized, non-uniform format, uneven quality, little descriptive text, only a few shots with limited length etc. Therefore, Internet is a potential repository to provide a reliable source for concept learning. In this paper, we focus on the semantic concept learning through known Internet video sources mining. Starting from the video-sharing websites, an automatical graph model generator for concepts relationship learning based on known ontology such as LSCOM, WordNet and ConceptNet is discussed. An automated source discovery method is addressed which prove to be useful in concept detection from the massive Internet videos. Experimental results prove that the addressed method is effective and efficient in semantic concept detection and learning through massive Internet video mining.

关键词： Internet Video sharing Video on demand Content based retrieval Learning systems Laboratories Information retrieval YouTube Data mining Conferences

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Pedestrian Detection Based on Hybrid Features

Pedestrian Detection Based on Hybrid Features

引用

International Symposium on intelligent Information technology Application, IITA

作者： Bin Hu Shengjin Wang Xiaoqing Ding State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing China

In this paper, we propose a new approach for pedestrian detection in crowded scene from static *** method is based on hybrid features, one type of middle-level features, which include Haar-like features and gradient features, two low-level feature sets. The haar-like features focus on the local edges information of the image and the gradient features focus on the local regions information. We use two stages of Adaboost to train the final classifier. In the first stage, the whole image is divided into many small windows which all include numerous low-level features. Adaboost is used in each window to get one mid-level feature which composes of some best features including Haar-like features and gradient features in this window. Secondly, from all midlevel features, Adaboost is used again to get the final classifier. Experiment results on common datasets and comparisons with some previous methods are given.

关键词： Object detection Support vector machines Support vector machine classification Laboratories Layout Robustness Lighting Detectors Humans Shape

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Maximum Margin Embedding

Maximum Margin Embedding

引用

IEEE International Conference on Data Mining (ICDM)

作者： Bin Zhao Fei Wang Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing China

We propose a new dimensionality reduction method called maximum margin embedding (MME), which targets to projecting data samples into the most discriminative subspace, where clusters are most well-separated. Specifically, MME projects input patterns onto the normal of the maximum margin separating hyperplanes. As a result, MME only depends on the geometry of the optimal decision boundary and not on the distribution of those data points lying further away from this boundary. Technically, MME is formulated as an integer programming problem and we propose a cutting plane algorithm to solve it. Moreover, we prove theoretically that the computational time of MME scales linearly with the dataset size. Experimental results on both toy and real world datasets demonstrate the effectiveness of MME.

关键词： Principal component analysis Data mining Linear programming Laboratories intelligent systems Information science Geometry Error analysis Cost function Probability distribution

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Fast Text Registration and Enhancement in Digital Video

Fast Text Registration and Enhancement in Digital Video

引用

International Conference on Computer Science and Information technology (CSIT)

作者： Ding Hui Ding Xiao-Qing Wang Sheng-Jin State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Electroni Engineering Tsinghua University Beijing China

This paper addresses a fast and reliable video text registration and enhancement algorithm suitable to tracking text and optical character recognition (OCR). The fast registration algorithm based on the normalized cross-correlation and pyramid structure in a coarse-to-fine scheme. After extracting reference text block, we use the text registration method to find the corresponding text blocks in next consecutive frames. By using intensity-hue-saturation (IHS) fusion method to enhance text block. This proposed algorithm working with rectangular sub-images, which obtained by down-sampling the frames at different levels of the pyramid, the speed of the algorithm can be increased. We have tested our match algorithm in a large set of experiments with video clips recorded from television and achieved good matching results.

关键词： Image color analysis Pixel Laboratories Algorithm design and analysis Feature extraction Manganese Computational modeling

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Web Image Clustering Based on Multi-instance

Web Image Clustering Based on Multi-instance

引用

IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jing Lu ShaoPing Ma Min Zhang State Key Laboratory of Intelligent Tech. & Systems Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology Beijing China

In image retrieval and annotation, multi-instance learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi-instance objects based on expectation maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users' interests on specific Web images without any manual labeled data. And comparative studies have shown the effectiveness of the proposed algorithms.

关键词： Image retrieval Clustering algorithms intelligent agent Partitioning algorithms Supervised learning Iterative algorithms Content based retrieval intelligent systems Information science Computer science

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A high-precision template localization algorithm using SIFT keypoints

A high-precision template localization algorithm using SIFT ...

引用

International International Symposium on Computer and Information Sciences (ISCIS)

作者： Yang Yang Yixu Song Muhammad Akram Shaikh Jiaxin Wang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and TechnologyDepartment of Computer Science Tsinghua University Beijing China

High-precision localization is one of the important applications in the field of computer vision. In this paper a high-precision template localization algorithm based on SIFT (scale invariant feature transform) is presented. The proposed method is composed of three main steps. In the initial step the SIFT features are extracted. With these features the basic matching strategy and clustering method similar distance threshold (SDT) are investigated to match the keypoints between template and test images and eliminate the possibility of mismatch. Then iterative least square method (ILSM) is adopted to locate the template and improve the accuracy. Compared with the traditional template matching methods, the proposed method could enhance the robustness effectively, which ensures to give correct results, no matter the test image changes its scale, rotates itself or is covered partly. The localization accuracy reaches 0.1 pixels.

关键词： Testing Feature extraction Image matching Application software Computer vision Robustness Bonding Laboratories Clustering algorithms Clustering methods

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Towards optimal query design for relevance feedback in image retrieval

Towards optimal query design for relevance feedback in image...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingyu Cui Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing China

We analyze the sub-optimality of traditional greedy active learning based relevance feedback methods in image retrieval, and propose a novel active learning approach to query labels of multiple images together, which minimize the needed round of feedbacks and achieve satisfactory result in a near optimal manner. Our experiments on real image retrieval demonstrate that our solution can yield comparable precession/recall rate by significantly less relevance feedbacks.

关键词： Image retrieval Support vector machines Support vector machine classification state feedback Error analysis Information retrieval Laboratories Learning systems Optimization methods Computational complexity

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Cost-sensitive boosting algorithms as gradient descent

Cost-sensitive boosting algorithms as gradient descent

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qu-Tang Cai Yang-Qui Song Chang-Shui Zhang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing China

AdaBoost is a well known boosting method for generating strong ensemble of weak base learners. The procedure of AdaBoost can be fitted in a gradient descent optimization framework, which is important for analyzing and devising its procedure. Cost sensitive boosting (CSB) is an emerging subject extending the boosting methods for cost sensitive classification applications. Most CSB methods are performed by directly modifying the original AdaBoost procedure. Unfortunately, the effectiveness of most cost sensitive boosting methods are checked only by experiments. It remains unclear whether these methods can be viewed as gradient descent procedures like AdaBoost. In this paper, we show that several typical CSB methods can also be view as gradient descent for minimizing a unified objective function. We then deduce a general greedy boosting procedure. Experimental results also validate the effectiveness of the proposed procedure.

关键词： Boosting Costs Laboratories intelligent systems Information science Automation Pattern recognition Classification algorithms Algorithm design and analysis

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Text Classification Based on Transfer Learning and Self-Training

Text Classification Based on Transfer Learning and Self-Trai...

引用

International Conference on Natural Computation (ICNC)

作者： Yabin Zheng Shaohua Teng Zhiyuan Liu Maosong Sun State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Traditional text classification methods make a basic assumption: the training and test set are homologous, while this naive assumption may not hold in the real world, especially in the Web environment. Documents on the Web change from time to time, pre-trained model may be out of date when applied to new emerging documents. However some information of training set is nonetheless useful. In this paper we proposed a novel method to discover the constant common knowledge in both training and test set by transfer learning, then a model is built based on this knowledge to fit the distribution in test set. The model is reinforced iteratively by adding most confident instances in unlabeled test set to training set until convergence, which is a self-training process, preliminary experiment shows that our method achieves an approximately 8.92% improvement as compared to the standard supervised-learning method.

关键词： Text categorization Convergence Machine learning Automatic testing Support vector machines Text mining Sun intelligent systems Information science Computer science

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COX-2 activity prediction in Chinese medicine using neural network based ensemble learning methods

COX-2 activity prediction in Chinese medicine using neural n...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wei Li Yannan Zhao Yixu Song Zehong Yang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science Tsinghua University Beijing China

In this paper, neural network based ensemble learning methods are introduced in predicting activities of COX-2 inhibitors in Chinese medicine quantitative structure-activity relationship (QSAR) research. Three different ensemble learning methods: bagging, boosting and random subspace are tested using neural networks as basic regression rules. Experiments show that all three methods, especially boosting, are fast and effective ways in the activity prediction of Chinese medicine QSAR research, which is generally based on a small amount of training samples.

关键词： Artificial neural networks Boosting Inhibitors Training Bagging Learning systems Support vector machines

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