2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06), 2006
Object detection can be posted as those classification tasks where the rare positive patterns are... more Object detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR) , and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods.
2009 10th International Conference on Document Analysis and Recognition, 2009
This paper proposes a novel hybrid method to robustly and accurately localize texts in natural sc... more This paper proposes a novel hybrid method to robustly and accurately localize texts in natural scene images. A text region detector is designed to generate a text confidence map, based on which text components can be segmented by local binarization approach. A Conditional Random Field (CRF) model, considering the unary component property as well as binary neighboring component relationship, is then presented to label components as "text" or "non-text". Last, text components are grouped into text lines with an energy minimization approach. Experimental results show that the proposed method gives promising performance comparing with the existing methods on ICDAR 2003 competition dataset. 2009 10th International Conference on Document Analysis and Recognition 978-0-7695-3725-2/09 $25.00
2008 The Eighth IAPR International Workshop on Document Analysis Systems, 2008
In this paper, we present a robust system to accurately detect and localize texts in natural scen... more In this paper, we present a robust system to accurately detect and localize texts in natural scene images. For text detection, a region-based method utilizing multiple features and cascade AdaBoost classifier is adopted. For text localization, a window grouping method integrating text line competition analysis is used to generate text lines. Then within each text line, local binarization is used to extract candidate connected components (CCs) and non-text CCs are filtered out by Markov Random Fields (MRF) model, through which text line can be localized accurately. Experiments on the public benchmark ICDAR 2003 Robust Reading and Text Locating Dataset 1 show that our system is comparable to the best existing methods both in accuracy and speed.
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
D ensity estimation is a fundamental problem in pattern recognition and machine learning. It is p... more D ensity estimation is a fundamental problem in pattern recognition and machine learning. It is particularly important for classification using the Bayes decision rule [1]. The methods for density estimation can be grouped into two categories: parametric and nonparametric. Parametric methods rely on functional density models but are compact in storage and computation. Nonparametric methods, such as Parzen windows and nearest neighbor methods, can adapt to arbitrary distributions but the model needs to store all the training points. The Gaussian mixture model (GMM) can be viewed as a semi-parametric approach. It is flexible to model irregular distributions and has moderate complexity. The Expectation Maximization (EM) algorithm [2-3], based on maximum likelihood, is a basic approach for mixture density estimation, and there has been many improved methods along this direction [1] .
2010 12th International Conference on Frontiers in Handwriting Recognition, 2010
This paper presents a text query-based method for keyword spotting from online Chinese handwritte... more This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple candidates of character patterns are generated by over-segmentation, and sequences of candidate characters are matched with the query word in beam search. The character classifier is trained by one-vs-all strategy so that it gives high similarity to the target class and low scores to the others. Particularly, we use a one-vs-all trained prototype classifier and a support vector machine (SVM) classifier for similarity scoring. The method yielded promising performance in experiments on a database containing 550 pages of 110 writers. For words of four characters, the recall, precision and F measure are 87. 25%, 94.84% and 90.88%, respectively.
2008 The Eighth IAPR International Workshop on Document Analysis Systems, 2008
We present an effective approach for grouping text lines in online handwritten Japanese documents... more We present an effective approach for grouping text lines in online handwritten Japanese documents by combining temporal and spatial information. Initially, strokes are grouped into text line strings according to off-stroke distances. Each text line string is segmented into text lines by dynamic programming (DP) optimizing a cost function trained by the minimum classification error (MCE) method. Over-segmented text lines are then merged with a support vector machine (SVM) classifier for making merge/non-merge decisions, and last, a spatial merge module corrects the segmentation errors caused by delayed strokes. In experiments on the TUAT Kondate database, the proposed approach achieves the Entity Detection Metric (EDM) rate of 0.8816, the Edit-Distance Rate (EDR) of 0.1234, which demonstrates the superiority of our approach.
The recognition of Indian and Arabic handwriting is drawing increasing attention in recent years.... more The recognition of Indian and Arabic handwriting is drawing increasing attention in recent years. To test the promise of existing handwritten numeral recognition methods and provide new benchmarks for future research, this paper presents some results of handwritten Bangla and Farsi numeral recognition on binary and gray-scale images. On proper pre-processing, feature extraction and classification, we achieved very high accuracies on three databases: ISI Bangla numerals, CENPARMI Farsi numerals, and IFHCDB Farsi numerals. The benefit of recognition on gray-scale images is justified.
This paper investigates a number of confidence transformation methods for measurement-level combi... more This paper investigates a number of confidence transformation methods for measurement-level combination of classifiers. Each confidence transformation method is the combination of a scaling function and an activation function. The activation functions correspond to different types of confidences: likelihood (exponential), log-likelihood, sigmoid, and the evidence combination of sigmoid measures. The sigmoid and evidence measures serve as approximates to class probabilities. The scaling functions are derived by Gaussian density modeling, logistic regression with variable inputs, etc. We test the confidence transformation methods in handwritten digit recognition by combining variable sets of classifiers: neural classifiers only, distance classifiers only, strong classifiers, and mixed strong/weak classifiers. The results show that confidence transformation is efficient to improve the combination performance in all the settings. The normalization of class probabilities to unity of sum is shown to be detrimental to the combination performance. Comparing the scaling functions, the Gaussian method and the logistic regression perform well in most cases. Regarding the confidence types, the sigmoid and evidence measures perform well in most cases, and the evidence measure generally outperforms the sigmoid measure. We also show that the confidence transformation methods are highly robust to the validation sample size in parameter estimation.
The classification performance of nearest prototype classifiers largely relies on the prototype l... more The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithm. The minimum classification error (MCE) method and the soft nearest prototype classifier (SNPC) method are two important algorithms using misclassification loss. This paper proposes a new prototype learning algorithm based on the conditional log-likelihood loss (CLL), which is based on the discriminative model called log-likelihood of margin (LOGM). A regularization term is added to avoid over-fitting in training as well as to maximize the hypothesis margin. The CLL in the LOGM algorithm is a convex function of margin, and so, shows better convergence than the MCE. In addition, we show the effects of distance metric learning with both prototype-dependent weighting and prototypeindependent weighting. Our empirical study on the benchmark datasets demonstrates that the LOGM algorithm yields higher classification accuracies than the MCE, generalized learning vector quantization (GLVQ), soft nearest prototype classifier (SNPC) and the robust soft learning vector quantization (RSLVQ), and moreover, the LOGM with prototype-dependent weighting achieves comparable accuracies to the support vector machine (SVM) classifier.
2009 10th International Conference on Document Analysis and Recognition, 2009
Text line segmentation in unconstrained handwritten documents remains a challenge because handwri... more Text line segmentation in unconstrained handwritten documents remains a challenge because handwritten text lines are multi-skewed and not obviously separated. This paper presents a new approach based on the variational Bayes (VB) framework for text line segmentation. Viewing the document image as a mixture density model, with each text line approximated by a Gaussian component, the VB method can automatically determine the number of components. We extend the VB method such that it can both eliminate and split components and control the orientation of text line lines. Experiments on Chinese handwritten documents demonstrated the effectiveness of the approach.
2009 10th International Conference on Document Analysis and Recognition, 2009
This paper describes a system for handwritten Chinese text recognition integrating language model... more This paper describes a system for handwritten Chinese text recognition integrating language model. On a text line image, the system generates character segmentation and word segmentation candidates, and the candidate paths are evaluated by character recognition scores and language model. The optimal path, giving segmentation and recognition result, is found using a pruned dynamic programming search method. We evaluate various language models, including the character-based n-gram, word-based ngram, and hybrid n-gram models. Experimental results on the HIT-HW database show that the language models improve the recognition performance remarkably.
2011 International Conference on Document Analysis and Recognition, 2011
In the Chinese handwriting recognition competition organized with the ICDAR 2011, four tasks were... more In the Chinese handwriting recognition competition organized with the ICDAR 2011, four tasks were evaluated: offline and online isolated character recognition, offline and online handwritten text recognition. To enable the training of recognition systems, we announced the large databases CASIA-HWDB/OLHWDB. The submitted systems were evaluated on un-open datasets to report character-level correct rates. In total, we received 25 systems submitted by eight groups. On the test datasets, the best results (correct rates) are 92.18% for offline character recognition, 95.77% for online character recognition, 77.26% for offline text recognition, and 94.33% for online text recognition, respectively. In addition to the evaluation results, we provide short descriptions of the recognition methods and have brief discussions.
2010 Chinese Conference on Pattern Recognition (CCPR), 2010
Chinese handwriting recognition remains a challenge. Research works have reported very high accur... more Chinese handwriting recognition remains a challenge. Research works have reported very high accuracies on neatly handwritten characters yet the performance on unconstrained handwriting remains quite low. To promote the recognition technology, new databases of unconstrained handwriting have been constructed for academic research and benchmarking. This paper reports the contest results of online and offline handwritten Chinese character recognition using the new generation of databases, targeting 3,755 Chinese characters of the GB2312-80 first level set. Nine systems from four groups were submitted for evaluation. The best results are 92.39% accuracy for online character recognition and 89.99% accuracy for offline character recognition. Detailed analysis of results on data of different writers reveals the diversity of writing quality. The future contests will consider continuous script recognition as well as isolated character recognition.
2009 10th International Conference on Document Analysis and Recognition, 2009
This paper describes a publicly available database, CASIA-OLHWDB1, for research on online handwri... more This paper describes a publicly available database, CASIA-OLHWDB1, for research on online handwritten Chinese character recognition. This database is the first of our series of online/offline handwritten characters and texts, collected using Anoto pen on paper. It contains unconstrained handwritten characters of 4,037 categories (3,866 Chinese characters and 171 symbols) produced by 420 persons, and 1,694,741 samples in total. It can be used for design and evaluation of character recognition algorithms and classifier design for handwritten text recognition systems. We have partitioned the samples into three grades and into training and test sets. Preliminary experiments on the database using a stateof-the-art recognizer justify the challenge of recognition. 2009 10th International Conference on Document Analysis and Recognition 978-0-7695-3725-2/09 $25.00
2010 12th International Conference on Frontiers in Handwriting Recognition, 2010
To reduce the classification errors of online handwritten Japanese character recognition, we prop... more To reduce the classification errors of online handwritten Japanese character recognition, we propose a method for confusing characters discrimination with little additional costs. After building confusing sets by cross validation using a baseline quadratic classifier, a logistic regression (LR) classifier is trained to discriminate the characters in each set. The LR classifier uses subspace features selected from existing vectors of the baseline classifier, thus has no extra parameters except the weights, which consumes a small storage space compared to the baseline classifier. In experiments on the TUAT HANDS databases with the modified quadratic discriminant function (MQDF) as baseline classifier, the proposed method has largely reduced the confusion caused by non-Kanji characters.
International Journal on Document Analysis and Recognition (IJDAR), 2010
This paper describes a robust model for on-line handwritten Japanese text recognition. The method... more This paper describes a robust model for on-line handwritten Japanese text recognition. The method evaluates the likelihood of candidate segmentation paths by combining scores of character pattern size, inner gap, character recognition, single-character position, pair-character position, likelihood of candidate segmentation point and linguistic context. The path score is insensitive to the number of candidate patterns and the optimal path can be found by the Viterbi search. In experiments of handwritten Japanese sentence recognition, the proposed method yielded superior performance.
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06), 2006
Object detection can be posted as those classification tasks where the rare positive patterns are... more Object detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR) , and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods.
2009 10th International Conference on Document Analysis and Recognition, 2009
This paper proposes a novel hybrid method to robustly and accurately localize texts in natural sc... more This paper proposes a novel hybrid method to robustly and accurately localize texts in natural scene images. A text region detector is designed to generate a text confidence map, based on which text components can be segmented by local binarization approach. A Conditional Random Field (CRF) model, considering the unary component property as well as binary neighboring component relationship, is then presented to label components as "text" or "non-text". Last, text components are grouped into text lines with an energy minimization approach. Experimental results show that the proposed method gives promising performance comparing with the existing methods on ICDAR 2003 competition dataset. 2009 10th International Conference on Document Analysis and Recognition 978-0-7695-3725-2/09 $25.00
2008 The Eighth IAPR International Workshop on Document Analysis Systems, 2008
In this paper, we present a robust system to accurately detect and localize texts in natural scen... more In this paper, we present a robust system to accurately detect and localize texts in natural scene images. For text detection, a region-based method utilizing multiple features and cascade AdaBoost classifier is adopted. For text localization, a window grouping method integrating text line competition analysis is used to generate text lines. Then within each text line, local binarization is used to extract candidate connected components (CCs) and non-text CCs are filtered out by Markov Random Fields (MRF) model, through which text line can be localized accurately. Experiments on the public benchmark ICDAR 2003 Robust Reading and Text Locating Dataset 1 show that our system is comparable to the best existing methods both in accuracy and speed.
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
D ensity estimation is a fundamental problem in pattern recognition and machine learning. It is p... more D ensity estimation is a fundamental problem in pattern recognition and machine learning. It is particularly important for classification using the Bayes decision rule [1]. The methods for density estimation can be grouped into two categories: parametric and nonparametric. Parametric methods rely on functional density models but are compact in storage and computation. Nonparametric methods, such as Parzen windows and nearest neighbor methods, can adapt to arbitrary distributions but the model needs to store all the training points. The Gaussian mixture model (GMM) can be viewed as a semi-parametric approach. It is flexible to model irregular distributions and has moderate complexity. The Expectation Maximization (EM) algorithm [2-3], based on maximum likelihood, is a basic approach for mixture density estimation, and there has been many improved methods along this direction [1] .
2010 12th International Conference on Frontiers in Handwriting Recognition, 2010
This paper presents a text query-based method for keyword spotting from online Chinese handwritte... more This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple candidates of character patterns are generated by over-segmentation, and sequences of candidate characters are matched with the query word in beam search. The character classifier is trained by one-vs-all strategy so that it gives high similarity to the target class and low scores to the others. Particularly, we use a one-vs-all trained prototype classifier and a support vector machine (SVM) classifier for similarity scoring. The method yielded promising performance in experiments on a database containing 550 pages of 110 writers. For words of four characters, the recall, precision and F measure are 87. 25%, 94.84% and 90.88%, respectively.
2008 The Eighth IAPR International Workshop on Document Analysis Systems, 2008
We present an effective approach for grouping text lines in online handwritten Japanese documents... more We present an effective approach for grouping text lines in online handwritten Japanese documents by combining temporal and spatial information. Initially, strokes are grouped into text line strings according to off-stroke distances. Each text line string is segmented into text lines by dynamic programming (DP) optimizing a cost function trained by the minimum classification error (MCE) method. Over-segmented text lines are then merged with a support vector machine (SVM) classifier for making merge/non-merge decisions, and last, a spatial merge module corrects the segmentation errors caused by delayed strokes. In experiments on the TUAT Kondate database, the proposed approach achieves the Entity Detection Metric (EDM) rate of 0.8816, the Edit-Distance Rate (EDR) of 0.1234, which demonstrates the superiority of our approach.
The recognition of Indian and Arabic handwriting is drawing increasing attention in recent years.... more The recognition of Indian and Arabic handwriting is drawing increasing attention in recent years. To test the promise of existing handwritten numeral recognition methods and provide new benchmarks for future research, this paper presents some results of handwritten Bangla and Farsi numeral recognition on binary and gray-scale images. On proper pre-processing, feature extraction and classification, we achieved very high accuracies on three databases: ISI Bangla numerals, CENPARMI Farsi numerals, and IFHCDB Farsi numerals. The benefit of recognition on gray-scale images is justified.
This paper investigates a number of confidence transformation methods for measurement-level combi... more This paper investigates a number of confidence transformation methods for measurement-level combination of classifiers. Each confidence transformation method is the combination of a scaling function and an activation function. The activation functions correspond to different types of confidences: likelihood (exponential), log-likelihood, sigmoid, and the evidence combination of sigmoid measures. The sigmoid and evidence measures serve as approximates to class probabilities. The scaling functions are derived by Gaussian density modeling, logistic regression with variable inputs, etc. We test the confidence transformation methods in handwritten digit recognition by combining variable sets of classifiers: neural classifiers only, distance classifiers only, strong classifiers, and mixed strong/weak classifiers. The results show that confidence transformation is efficient to improve the combination performance in all the settings. The normalization of class probabilities to unity of sum is shown to be detrimental to the combination performance. Comparing the scaling functions, the Gaussian method and the logistic regression perform well in most cases. Regarding the confidence types, the sigmoid and evidence measures perform well in most cases, and the evidence measure generally outperforms the sigmoid measure. We also show that the confidence transformation methods are highly robust to the validation sample size in parameter estimation.
The classification performance of nearest prototype classifiers largely relies on the prototype l... more The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithm. The minimum classification error (MCE) method and the soft nearest prototype classifier (SNPC) method are two important algorithms using misclassification loss. This paper proposes a new prototype learning algorithm based on the conditional log-likelihood loss (CLL), which is based on the discriminative model called log-likelihood of margin (LOGM). A regularization term is added to avoid over-fitting in training as well as to maximize the hypothesis margin. The CLL in the LOGM algorithm is a convex function of margin, and so, shows better convergence than the MCE. In addition, we show the effects of distance metric learning with both prototype-dependent weighting and prototypeindependent weighting. Our empirical study on the benchmark datasets demonstrates that the LOGM algorithm yields higher classification accuracies than the MCE, generalized learning vector quantization (GLVQ), soft nearest prototype classifier (SNPC) and the robust soft learning vector quantization (RSLVQ), and moreover, the LOGM with prototype-dependent weighting achieves comparable accuracies to the support vector machine (SVM) classifier.
2009 10th International Conference on Document Analysis and Recognition, 2009
Text line segmentation in unconstrained handwritten documents remains a challenge because handwri... more Text line segmentation in unconstrained handwritten documents remains a challenge because handwritten text lines are multi-skewed and not obviously separated. This paper presents a new approach based on the variational Bayes (VB) framework for text line segmentation. Viewing the document image as a mixture density model, with each text line approximated by a Gaussian component, the VB method can automatically determine the number of components. We extend the VB method such that it can both eliminate and split components and control the orientation of text line lines. Experiments on Chinese handwritten documents demonstrated the effectiveness of the approach.
2009 10th International Conference on Document Analysis and Recognition, 2009
This paper describes a system for handwritten Chinese text recognition integrating language model... more This paper describes a system for handwritten Chinese text recognition integrating language model. On a text line image, the system generates character segmentation and word segmentation candidates, and the candidate paths are evaluated by character recognition scores and language model. The optimal path, giving segmentation and recognition result, is found using a pruned dynamic programming search method. We evaluate various language models, including the character-based n-gram, word-based ngram, and hybrid n-gram models. Experimental results on the HIT-HW database show that the language models improve the recognition performance remarkably.
2011 International Conference on Document Analysis and Recognition, 2011
In the Chinese handwriting recognition competition organized with the ICDAR 2011, four tasks were... more In the Chinese handwriting recognition competition organized with the ICDAR 2011, four tasks were evaluated: offline and online isolated character recognition, offline and online handwritten text recognition. To enable the training of recognition systems, we announced the large databases CASIA-HWDB/OLHWDB. The submitted systems were evaluated on un-open datasets to report character-level correct rates. In total, we received 25 systems submitted by eight groups. On the test datasets, the best results (correct rates) are 92.18% for offline character recognition, 95.77% for online character recognition, 77.26% for offline text recognition, and 94.33% for online text recognition, respectively. In addition to the evaluation results, we provide short descriptions of the recognition methods and have brief discussions.
2010 Chinese Conference on Pattern Recognition (CCPR), 2010
Chinese handwriting recognition remains a challenge. Research works have reported very high accur... more Chinese handwriting recognition remains a challenge. Research works have reported very high accuracies on neatly handwritten characters yet the performance on unconstrained handwriting remains quite low. To promote the recognition technology, new databases of unconstrained handwriting have been constructed for academic research and benchmarking. This paper reports the contest results of online and offline handwritten Chinese character recognition using the new generation of databases, targeting 3,755 Chinese characters of the GB2312-80 first level set. Nine systems from four groups were submitted for evaluation. The best results are 92.39% accuracy for online character recognition and 89.99% accuracy for offline character recognition. Detailed analysis of results on data of different writers reveals the diversity of writing quality. The future contests will consider continuous script recognition as well as isolated character recognition.
2009 10th International Conference on Document Analysis and Recognition, 2009
This paper describes a publicly available database, CASIA-OLHWDB1, for research on online handwri... more This paper describes a publicly available database, CASIA-OLHWDB1, for research on online handwritten Chinese character recognition. This database is the first of our series of online/offline handwritten characters and texts, collected using Anoto pen on paper. It contains unconstrained handwritten characters of 4,037 categories (3,866 Chinese characters and 171 symbols) produced by 420 persons, and 1,694,741 samples in total. It can be used for design and evaluation of character recognition algorithms and classifier design for handwritten text recognition systems. We have partitioned the samples into three grades and into training and test sets. Preliminary experiments on the database using a stateof-the-art recognizer justify the challenge of recognition. 2009 10th International Conference on Document Analysis and Recognition 978-0-7695-3725-2/09 $25.00
2010 12th International Conference on Frontiers in Handwriting Recognition, 2010
To reduce the classification errors of online handwritten Japanese character recognition, we prop... more To reduce the classification errors of online handwritten Japanese character recognition, we propose a method for confusing characters discrimination with little additional costs. After building confusing sets by cross validation using a baseline quadratic classifier, a logistic regression (LR) classifier is trained to discriminate the characters in each set. The LR classifier uses subspace features selected from existing vectors of the baseline classifier, thus has no extra parameters except the weights, which consumes a small storage space compared to the baseline classifier. In experiments on the TUAT HANDS databases with the modified quadratic discriminant function (MQDF) as baseline classifier, the proposed method has largely reduced the confusion caused by non-Kanji characters.
International Journal on Document Analysis and Recognition (IJDAR), 2010
This paper describes a robust model for on-line handwritten Japanese text recognition. The method... more This paper describes a robust model for on-line handwritten Japanese text recognition. The method evaluates the likelihood of candidate segmentation paths by combining scores of character pattern size, inner gap, character recognition, single-character position, pair-character position, likelihood of candidate segmentation point and linguistic context. The path score is insensitive to the number of candidate patterns and the optimal path can be found by the Viterbi search. In experiments of handwritten Japanese sentence recognition, the proposed method yielded superior performance.
Uploads
Papers by Cheng-Lin Liu