2011 10th International Conference on Machine Learning and Applications and Workshops最新文献_第4页

Estimation of PSF for a Shaking Blurred Image Using GA 用遗传算法估计抖动模糊图像的PSF

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.165

Takumi Shimomukai, M. Yoshioka, H. Yanagimoto

引用次数: 1

Multiple Nonlinear Subspace Methods Using Subspace-based Support Vector Machines 基于子空间支持向量机的多非线性子空间方法

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.100

Takuya Kitamura, S. Abe, Yusuke Tanaka

引用次数: 1

Combining Visual and Acoustic Features for Music Genre Classification 结合视觉与听觉特征的音乐类型分类

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.48

Ming-Ju Wu, Zhi-Sheng Chen, J. Jang, Jia-Min Ren, Yi-Hsung Li, Chun-Hung Lu

引用次数: 65

Using Genre Interest of Users to Detect Profile Injection Attacks in Movie Recommender Systems 利用用户类型兴趣检测电影推荐系统中的配置文件注入攻击

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.151

Ghazaleh Aghili, M. Shajari, Shahram Khadivi, M. Morid

引用次数: 10

Efficient Approximate Semi-supervised Support Vector Machines through Submodular Optimization 基于次模优化的高效近似半监督支持向量机

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.62

Wael Emara, M. Kantardzic

引用次数: 0

Predicting Patients Likely to Overstay in Hospitals 预测病人可能在医院逾期居留

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.115

R. Vivanco, D. Roberts

引用次数: 4

Active Batch Selection for Fuzzy Classification in Facial Expression Recognition 面部表情识别中模糊分类的主动批选择

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.22

Shayok Chakraborty, Hemanth Venkateswara, V. Balasubramanian, S. Panchanathan

{"title":"Active Batch Selection for Fuzzy Classification in Facial Expression Recognition","authors":"Shayok Chakraborty, Hemanth Venkateswara, V. Balasubramanian, S. Panchanathan","doi":"10.1109/ICMLA.2011.22","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.22","url":null,"abstract":"Automated recognition of facial expressions is an important problem in computer vision applications. Due to the vagueness in class definitions, expression recognition is often conceived as a fuzzy label problem. Annotating a data point in such a problem involves significant manual effort. Active learning techniques are effective in reducing human labeling effort to induce a classification model as they automatically select the salient and exemplar instances from vast amounts of unlabeled data. Further, to address the high redundancy in data such as image or video sequences as well as to account for the presence of multiple labeling agents, there have been recent attempts towards a batch mode form of active learning where a batch of data points is selected simultaneously from an unlabeled set. In this paper, we propose a novel optimization-based batch mode active learning technique for fuzzy label classification problems. To the best of our knowledge, this is the ï¬rst effort to develop such a scheme primarily intended for the fuzzy label context. The proposed algorithm is computationally simple, easy to implement and has provable performance bounds. Our results on facial expression datasets corroborate the efficacy of the framework in reducing human annotation effort in real world recognition applications involving fuzzy labels.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126342380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Automatic Dishware Inspection: Applications and Comparisons of Two New Methods 自动餐具检测:两种新方法的应用与比较

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.40

Trung H. Duong, Mohsen Emami, L. L. Hoberock

{"title":"Automatic Dishware Inspection: Applications and Comparisons of Two New Methods","authors":"Trung H. Duong, Mohsen Emami, L. L. Hoberock","doi":"10.1109/ICMLA.2011.40","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.40","url":null,"abstract":"Commercial dishwashing systems currently involve human loading, sorting, inspecting, and unloading dishes and silverware pieces before and after washing in hot and humid environments. In such difficult working conditions, leading to high turn-over of low-paid employees, automation is desirable, especially in large-scale kitchens of hospitals, navy ships, schools, hotels and other dining facilities. Our project is a part of developing an integrated machine vision sorting and inspecting system for mixed dish pieces and silverware exiting a flight-type commercial dishwashing machine, coupled with automatic loading and unloading. We propose two new methods for automatically inspecting dish cleanliness, namely adaptive thresholding and maximum saliency map. On the first method, a new technique using partitioning and adaptive thresholding, combined with global thresholding are introduced. On the second method, we propose a new normalization technique. Both algorithms are fast, simple, and produce results invariant with lighting conditions and dish rotation about the camera-dish axis. Algorithms are written and tested by MatlabÒ R14 and Image Processing Toolbox V5.0 to 110 dish images taken in different lighting condition using different position of 51 separate dishes (either clean or dirty) of our dish set, in which 77 images are from dirty dishes with 799 dirty points in these dishes. The adaptive thresholding method produces 95.0% and 96.5% accuracies in discriminating clean from dirty dishes and dirty spot detection, respectively. While the maximum saliency map method produces 100% accuracies in discriminating clean from dirty dishes and 93.5% accuracies in and dirty spot detection.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127926368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Dynamic Testing and Calibration of Gaussian Processes for Vehicle Attitude Estimation 车辆姿态估计高斯过程的动态测试与标定

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.61

J. Britt, D. Broderick, D. Bevly, J. Hung

引用次数: 1

Error Bounds for Online Predictions of Linear-Chain Conditional Random Fields: Application to Activity Recognition for Users of Rolling Walkers 线性链条件随机场在线预测的误差界:在滚动助行器用户活动识别中的应用

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.64

M. Sinn, P. Poupart

{"title":"Error Bounds for Online Predictions of Linear-Chain Conditional Random Fields: Application to Activity Recognition for Users of Rolling Walkers","authors":"M. Sinn, P. Poupart","doi":"10.1109/ICMLA.2011.64","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.64","url":null,"abstract":"Linear-Chain Conditional Random Fields (L-CRFs) are a versatile class of models for the distribution of a sequence of hidden states (\"labels\") conditional on a sequence of observable variables. In general, the exact conditional marginal distributions of the labels can be computed only after the complete sequence of observations has been obtained, which forbids the prediction of labels in an online fashion. This paper considers approximations of the marginal distributions which only take into account past observations and a small number of observations in the future. Based on these approximations, labels can be predicted close to real-time. We establish rigorous bounds for the marginal distributions which can be used to assess the approximation error at runtime. We apply the results to an L-CRF which recognizes the activity of rolling walker users from a stream of sensor data. It turns out that if we allow for a prediction delay of half of a second, the online predictions achieve almost the same accuracy as the offline predictions based on the complete observation sequences.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133209774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2