Hui, Ka Yu. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 101-106). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Data sets with rich topical structure are common in many real world text classification tasks. A single data set often contains a wide variety of topics and, in a typical task, documents belonging to each class are dispersed across many of the topics. Often, a complex relationship exists between the topic a document discusses and the class label: positive or negative sentiment is expressed in documents from many different topics, but knowing the topic does not necessarily help in determining the sentiment label. We know from tasks such as Domain Adaptation that sentiment is expressed in different ways under different topics. Topical context can in some cases even reverse the sentiment polarity of words: to be sharp is a good quality for knives but bad for singers. This property can be found in many different document classification tasks. Standard document classification algorithms do not account for or take advantage of topical diversity; instead, classifiers are usually trained with the tacit assumption that topical diversity does not play a role. This thesis is focused on the interplay between the topical structure of corpora, how the target labels in a classification task distribute over the topics and how the topical structure can be utilised in building ensemble models for text classification. We show empirically that a dataset with rich topical structure can be problematic for single classifiers, and we develop two novel ensemble models to address the issues. We focus on two document classification tasks: document level sentiment analysis of product reviews and hierarchical categorisation of news text. For each task we develop a novel ensemble method that utilises topic models to address the shortcomings of traditional text classification algorithms. Our contribution is in showing empirically that the class association of document features is topic dependent. We show that using the topical context of documents for building ensembles is beneficial for some tasks, and present two new ensemble models for document classification. We also provide a fresh viewpoint for reasoning about the relationship of class labels, topical categories and document features.

Li Ngai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter Chapter 1. --- Introduction --- p.1-1 / Chapter 1.1. --- A Brief Review on Pattern Matching --- p.1-1 / Chapter 1.2. --- Objective of the Research Work --- p.1-5 / Chapter 1.3. --- Organization of the Thesis --- p.1-6 / Chapter 1.4. --- Notes on Publications --- p.1-7 / Chapter Chapter 2. --- Background Information --- p.2-1 / Chapter 2.1. --- Introduction --- p.2-1 / Chapter 2.2. --- Review of Block Based Pattern Matching --- p.2-3 / Chapter 2.2.1 --- Gradient Descent Strategy --- p.2-3 / Chapter 2.2.2 --- Simplified Matching Operations --- p.2-10 / Chapter 2.2.3 --- Fast Full-Search Methods --- p.2-14 / Chapter 2.2.4 --- Transform-domain Manipulations --- p.2-19 / Chapter Chapter 3. --- Statistical Rejection Threshold for Pattern Matching --- p.3-1 / Chapter 3.1. --- Introduction --- p.3-1 / Chapter 3.2. --- Walsh Hadamard Transform --- p.3-3 / Chapter 3.3. --- Coarse-to-fine Pattern Matching in Walsh Hadamard Domain --- p.3-4 / Chapter 3.3.1. --- Bounding Euclidean Distance in Walsh Hadamard Domain --- p.3-5 / Chapter 3.3.2. --- Fast Projection Scheme --- p.3-9 / Chapter 3.3.3. --- Using the Projection Scheme for Pattern Matching --- p.3-17 / Chapter 3.4. --- Statistical Rejection Threshold --- p.3-18 / Chapter 3.5. --- Experimental Results --- p.3-22 / Chapter 3.6. --- Conclusions --- p.3-29 / Chapter 3.7. --- Notes on Publication --- p.3-30 / Chapter Chapter 4. --- Fast Walsh Search --- p.4-1 / Chapter 4.1. --- Introduction --- p.4-1 / Chapter 4.2. --- Approximating Sum-of-absolute Difference Using PS AD --- p.4-3 / Chapter 4.3. --- Two-level Threshold Scheme --- p.4-6 / Chapter 4.4. --- Block Matching Using SADDCC --- p.4-10 / Chapter 4.5. --- Optimization of Threshold and Number of Coefficients in PSAD --- p.4-15 / Chapter 4.6. --- Candidate Elimination by the Mean of PSAD --- p.4-23 / Chapter 4.7. --- Computation Requirement --- p.4-28 / Chapter 4.8. --- Experimental Results --- p.4-32 / Chapter 4.9. --- Conclusions --- p.4-45 / Chapter 4.10. --- Notes on Publications --- p.4-46 / Chapter Chapter 5. --- Conclusions & Future Works --- p.5-1 / Chapter 5.1. --- Contributions and Conclusions --- p.5-1 / Chapter 5.1.1. --- Statistical Rejection Threshold for Pattern Matching --- p.5-2 / Chapter 5.1.2. --- Fast Walsh Search --- p.5-3 / Chapter 5.2. --- Future Works --- p.5-4 / References --- p.I

by Liu Zhiyong. / "June 2003." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (p. 125-136). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

近年來，在公開的大規模人工標注數據集上訓練通用行人檢測器的方法有了顯著的進步。然而，當通用行人檢測器被應用到一個特定的，未公開過的場景中時，它的性能會不如預期。這是由待檢測的數據（源樣本）與訓練數據（目標樣本）的不匹配，以及新場景中視角、光照、分辨率和背景噪音的變化擾動造成的。 / 在本論文中，我們提出一個新的自動將通用行人檢測器適應到特定場景中的框架。這個框架分為兩個階段。在第一階段，我們探索監控錄像場景中提供的特定表征。利用這些表征，從目標場景中選擇正負樣本並重新訓練行人檢測器，該過程不斷迭代直至收斂。在第二階段，我們提出一個新的機器學習框架，該框架綜合每個樣本的標簽和比重。根據這些比重，源樣本和目標樣本被重新權重，以優化最終的分類器。這兩種方法都屬於半監督學習，僅僅需要非常少的人工干預。 / 使用提出的方法可以顯著提高通用行人檢測器的准確性。實驗顯示，由方法訓練出來的檢測器可以和使用大量手工標注的目標場景數據訓練出來的媲美。與其它解決類似問題的方法比較，該方法同樣好於許多已有方法。 / 本論文的工作已經分別於朲朱朱年和朲朱朲年在杉杅杅杅計算機視覺和模式識別會議（权杖材杒）中發表。 / In recent years, significant progress has been made in learning generic pedestrian detectors from publicly available manually labeled large scale training datasets. However, when a generic pedestrian detector is applied to a specific, previously undisclosed scene where the testing data (target examples) does not match with the training data (source examples) because of variations of viewpoints, resolutions, illuminations and backgrounds, its accuracy may decrease greatly. / In this thesis, a new framework is proposed automatically adapting a pre-trained generic pedestrian detector to a specific traffic scene. The framework is two-phased. In the first phase, scene-specific cues in the video surveillance sequence are explored. Utilizing the multi-cue information, both condent positive and negative examples from the target scene are selected to re-train the detector iteratively. In the second phase, a new machine learning framework is proposed, incorporating not only example labels but also example confidences. Source and target examples are re-weighted according to their confidence, optimizing the performance of the final classifier. Both methods belong to semi-supervised learning and require very little human intervention. / The proposed approaches significantly improve the accuracy of the generic pedestrian detector. Their results are comparable with the detector trained using a large number of manually labeled frames from the target scene. Comparison with other existing approaches tackling similar problems shows that the proposed approaches outperform many contemporary methods. / The works have been published on the IEEE Conference on Computer Vision and Pattern Recognition in 2011 and 2012, respectively. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wang, Meng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 42-45). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- PedestrianDetection --- p.1 / Chapter 1.1.1 --- Overview --- p.1 / Chapter 1.1.2 --- StatisticalLearning --- p.1 / Chapter 1.1.3 --- ObjectRepresentation --- p.2 / Chapter 1.1.4 --- SupervisedStatisticalLearninginObjectDetection --- p.3 / Chapter 1.2 --- PedestrianDetectioninVideoSurveillance --- p.4 / Chapter 1.2.1 --- ProblemSetting --- p.4 / Chapter 1.2.2 --- Challenges --- p.4 / Chapter 1.2.3 --- MotivationsandContributions --- p.5 / Chapter 1.3 --- RelatedWork --- p.6 / Chapter 1.4 --- OrganizationsofChapters --- p.9 / Chapter 2 --- Label Inferring by Multi-Cues --- p.10 / Chapter 2.1 --- DataSet --- p.10 / Chapter 2.2 --- Method --- p.12 / Chapter 2.2.1 --- CondentPositiveExamplesofPedestrians --- p.13 / Chapter 2.2.2 --- CondentNegativeExamplesfromtheBackground --- p.17 / Chapter 2.2.3 --- CondentNegativeExamplesfromVehicles --- p.17 / Chapter 2.2.4 --- FinalSceneSpecicPedestrianDetector --- p.19 / Chapter 2.3 --- ExperimentResults --- p.20 / Chapter 3 --- Transferring a Detector by Condence Propagation --- p.24 / Chapter 3.1 --- Method --- p.25 / Chapter 3.1.1 --- Overview --- p.25 / Chapter 3.1.2 --- InitialEstimationofCondenceScores --- p.27 / Chapter 3.1.3 --- Re-weightingSourceSamples --- p.27 / Chapter 3.1.4 --- Condence-EncodedSVM --- p.30 / Chapter 3.2 --- Experiments --- p.33 / Chapter 3.2.1 --- Datasets --- p.33 / Chapter 3.2.2 --- ParameterSetting --- p.35 / Chapter 3.2.3 --- Results --- p.36 / Chapter 4 --- Conclusions and Future Work --- p.40

行人再識別的主要任務是匹配不交叉的監控攝像頭中觀測到的行人。隨著監控攝像頭的普遍，這是一個非常重要的任務。並且，它是其他很多任務的重要子任務，例如跨攝像頭的跟蹤。行人再識別的難度存在於不同攝像頭中觀測到的同一個人會有很大的變化。這些變化來自於觀察角度的不同，光照的不同，和行人姿態的變化等等。在本文中，我們希望從如下的方面來重新思考並解決這個問題。 / 首先，我們發現當待匹配集合增大的時候，匹配的難度大幅度增加。在實際應用中，我們可以通過時間上的推演來減少待匹配集合的大小，簡化行人再識別這個問題。現有通過機器學習的方法來解決這個問題的算法基本會假設一個全局固定的度量。我們的方法來自提出於對於不同的待匹配集合應該有不同的度量的新觀點。因此，我們把這個問題重新定義在一個遷移學習的框架下。給定一個較大的訓練集合，我們通過訓練集合的樣本與當前的查詢集合和待匹配集合的相似程度，重新對訓練集合進行加權。這樣，我們提出一個加權的最大化邊界的度量學習方法，而這個度量較全訓練集共享的整體度量更加的具體。 / 我們進一步發現，在兩個不同的鏡頭中，物體形態的變換很難通過一個單一模型來進行描述。為了解決這一個問題，我們提出一個混合專家模型，要將圖片的空間進行進一步細化。我們的算法將剖分圖形空間和在每個細分後的空間中學習一個跨鏡頭的變換來將特征進行對齊。測試時，新樣本會與現有的“專家“模型進行匹配，選擇合適的變換。 我們通過一個稀疏正則項和最小信息損失正則項來進行約束。 / 在對上面各種方法的分析中，我們發現提取特征和訓練模型總是分開進行。一個更好的方法是將模型的訓練和特征提取同時進行。為此，我們希望能夠使用卷積神經網絡 來實現這個目標。通過精心設計網絡結構，底層網絡能夠通過兩組一一對應的特征來描 述圖像的局部信息。而這種信息對於匹配人的顏色紋理等特徵更加適合。在較高的層我 們希望學習到人在空間上的位移來判斷局部的位移是符合於人在不同攝像頭中的位移。 通過這些信息，我們的模型來決定這兩張圖片是否來自于同一個人。 / 在以上三個部分中，我們都同最先進的度量學習和其他基于特征設計的行人再識別方法進行比較。我們在不同的數據集上均取得了較為優秀的效果。我們進一步建立了一 個大規模的數據集，這個數據集包含更多的視角、更多的人且每個人在不同的視角下有 更多的圖片。 / Person re-identification is to match persons observed in non-overlapping camera views with visual features. This is an important task in video surveillance by itself and serves as metatask for other problems like inter-camera tracking. Challenges lie in the dramatic intra-person variation introduced by viewpoint change, illumination change and pose variation etc. In this thesis, we are trying to tackle this problem in the following aspects: / Firstly, we observe that the ambiguity increases with the number of candidates to be distinguished. In real world scenario, temporal reasoning is available and can simplify the problem by pruning the candidate set to be matched. Existing approaches adopt a fixed metric for matching all the subjects. Our approach is motivated by the insight that different visual metrics should be optimally learned for different candidate sets. The problem is further formulated under a transfer learning framework. Given a large training set, the training samples are selected and re-weighted according to their visual similarities with the query sample and its candidate set. A weighted maximum margin metric is learned and transferred from a generic metric to a candidate-set-specific metric. / Secondly, we observe that the transformations between two camera views may be too complex to be uni-modal. To tackle this, we propose to partition the image space and formulate the problem into a mixture of expert framework. Our algorithm jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross-view transforms. The visual features of an image pair from different views are locally aligned by being projected to a common feature space and then matched with softly assigned metrics which are locally optimized. The features optimal for recognizing identities are different from those for clustering cross-view transforms. They are jointly learned by utilizing sparsity-inducing norm and information theoretical regularization. / In all the above analysis, feature extraction and learning models are separately designed. A better idea is to directly learn features from training samples and those features can be applied to directly train a discriminative models. We propose a new model where feature extraction is jointly learned with a discriminative convolutional neural network. Local filters at the bottom layer can well extract the information useful for matching persons across camera views like color and texture. Higher layers will capture the spatial shift of those local patches. Finally, we will test whether the shift patterns of those local patches conform to the intra-camera variation of the same person. / In all three parts, comparisons with the state-of-the-art metric learning algorithms and person re-identification methods are carried out and our approach shows the superior performance on public benchmark dataset. Furthermore, we are building a much larger dataset that addresses the real-world scenario which contains much more camera views, identities, and images perview. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Li, Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 63-68). / Abstracts also in Chinese. / Acknowledgments --- p.iii / Abstract --- p.vii / Contents --- p.xii / List of Figures --- p.xiv / List of Tables --- p.xv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Person Re-Identification --- p.1 / Chapter 1.2 --- Challenge in Person Re-Identification --- p.2 / Chapter 1.3 --- Literature Review --- p.4 / Chapter 1.3.1 --- Feature Based Person Re-Identification --- p.4 / Chapter 1.3.2 --- Learning Based Person Re-Identification --- p.7 / Chapter 1.4 --- Thesis Organization --- p.8 / Chapter 2 --- Tranferred Metric Learning for Person Re-Identification --- p.10 / Chapter 2.1 --- Introduction --- p.10 / Chapter 2.2 --- Related Work --- p.12 / Chapter 2.2.1 --- Transfer Learning --- p.12 / Chapter 2.3 --- Our Method --- p.13 / Chapter 2.3.1 --- Visual Features --- p.13 / Chapter 2.3.2 --- Searching and Weighting Training Samples --- p.13 / Chapter 2.3.3 --- Learning Adaptive Metrics by Maximizing Weighted Margins --- p.15 / Chapter 2.4 --- Experimental Results --- p.17 / Chapter 2.4.1 --- Dataset Description --- p.17 / Chapter 2.4.2 --- Generic Metric Learning --- p.18 / Chapter 2.4.3 --- Transferred Metric Learning --- p.19 / Chapter 2.5 --- Conclusions and Discussions --- p.21 / Chapter 3 --- Locally Aligned Feature Transforms for Person Re-Identification --- p.23 / Chapter 3.1 --- Introduction --- p.23 / Chapter 3.2 --- Related Work --- p.24 / Chapter 3.2.1 --- Localized Methods --- p.25 / Chapter 3.3 --- Model --- p.26 / Chapter 3.4 --- Learning --- p.27 / Chapter 3.4.1 --- Priors --- p.27 / Chapter 3.4.2 --- Objective Function --- p.29 / Chapter 3.4.3 --- Training Model --- p.29 / Chapter 3.4.4 --- Multi-Shot Extension --- p.30 / Chapter 3.4.5 --- Discriminative Metric Learning --- p.31 / Chapter 3.5 --- Experiment --- p.32 / Chapter 3.5.1 --- Identification with Two Fixed Camera Views --- p.33 / Chapter 3.5.2 --- More General Camera Settings --- p.37 / Chapter 3.6 --- Conclusions --- p.38 / Chapter 4 --- Deep Neural Network for Person Re-identification --- p.39 / Chapter 4.1 --- Introduction --- p.39 / Chapter 4.2 --- Related Work --- p.43 / Chapter 4.3 --- Introduction of the New Dataset --- p.44 / Chapter 4.4 --- Model --- p.46 / Chapter 4.4.1 --- Architecture Overview --- p.46 / Chapter 4.4.2 --- Convolutional and Max-Pooling Layer --- p.48 / Chapter 4.4.3 --- Patch Matching Layer --- p.49 / Chapter 4.4.4 --- Maxout Grouping Layer --- p.52 / Chapter 4.4.5 --- Part Displacement --- p.52 / Chapter 4.4.6 --- Softmax Layer --- p.53 / Chapter 4.5 --- Training Strategies --- p.54 / Chapter 4.5.1 --- Data Augmentation and Balancing --- p.55 / Chapter 4.5.2 --- Bootstrapping --- p.55 / Chapter 4.6 --- Experiment --- p.56 / Chapter 4.6.1 --- Model Specification --- p.56 / Chapter 4.6.2 --- Validation on Single Pair of Cameras --- p.57 / Chapter 4.7 --- Conclusion --- p.58 / Chapter 5 --- Conclusion --- p.60 / Chapter 5.1 --- Conclusion --- p.60 / Chapter 5.2 --- Future Work --- p.61 / Bibliography --- p.63

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Thesis (M.Sc.)(Computer Science)--University of Pretoria, 2006. / Includes summary. Includes bibliographical references (leaves 164-174). Available on the Internet via the World Wide Web.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 118-123).

Thesis (M.Sc.)--York University, 2003. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 104-111). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99410