Case-based reasoning classifier based on learning pseudo metric retrieval

In case-based reasoning (CBR) classification systems, the similarity metrics play a key role and directly affect the system's performance. Based on our previous work on the learning pseudo metrics (LPM), we propose a case-based reasoning method for pattern classification, where the widely used Euclidean distance is replaced by the LPM to measure the closeness between the target case and each source case. The same type of case as the target case can be retrieved and the category of the target case can be defined by using the majority of reuse principle. Experimental results over some benchmark datasets and a fault diagnosis of the Tennessee-Eastman (TE) process demonstrate that the proposed reasoning techniques in this paper can effectively improve the classification accuracy, and the LPM-based retrieval method can substantially improve the quality and learning ability of CBR classifiers.     

Collaborative image retrieval via regularized metric learning

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring the correlation between low-level image features and the log data of users' relevance judgments. Compared to the previous research, a regularization mechanism is used in our algorithm to effectively prevent overfitting. Meanwhile, we formulate the proposed learning algorithm into a semidefinite programming problem, which can be solved very efficiently by existing software packages and is scalable to the size of log data. An extensive set of experiments has been conducted to show that the new algorithm can substantially improve the retrieval accuracy of a baseline CBIR system using Euclidean distance metric, even with a modest amount of log data. The experiment also indicates that the new algorithm is more effective and more efficient than two alternative algorithms, which exploit log data for image retrieval.     

Visual tracking via online discriminative multiple instance metric learning

Motion object tracking is an important issue in computer vision. In this paper, a robust tracking algorithm based on multiple instance learning (MIL) is proposed. First, a coarse-to-fine search method is designed to reduce the computation load of cropping candidate samples for a new arriving frame. Then, a bag-level similarity metric is proposed to select the most correct positive instances to form the positive bag. The instance’s importance to bag probability is determined by their Mahalanobis distance. Furthermore, an online discriminative classifier selection method, which exploits the average gradient and average weak classifiers strategy to optimize the margin function between positive and negative bags, is presented to solve the suboptimal problem in the process of selecting weak classifiers. Experimental results on challenging sequences show that the proposed method is superior to other compared methods in terms of both qualitative and quantitative assessments.     

Online classifier adaptation for cost-sensitive learning

In this paper, we propose the problem of online cost-sensitive classifier adaptation and the first algorithm to solve it. We assume that we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The problem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given an input data sample and the cost of misclassifying it, we update the adaptation function parameter by minimizing cost-weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them on classification performances, but also requires significantly less running time.

     

A multiple-point spatially weighted k-NN classifier for remote sensing

A novel classification method based on multiple-point statistics (MPS) is proposed in this article. The method is a modified version of the spatially weighted k-nearest neighbour (k-NN) classifier, which accounts for spatial correlation through weights applied to neighbouring pixels. The MPS characterizes the spatial correlation between multiple points of land-cover classes by learning local patterns in a training image. This rich spatial information is then converted to multiple-point probabilities and incorporated into the k-NN classifier. Experiments were conducted in two study areas, in which the proposed method for classification was tested on a WorldView-2 sub-scene of the Sichuan mountainous area and an IKONOS image of the Beijing urban area. The multiple-point weighted k-NN method (MPk-NN) was compared to several alternatives; including the traditional k-NN and two previously published spatially weighted k-NN schemes; the inverse distance weighted k-NN, and the geostatistically weighted k-NN. The classifiers using the Bayesian and Support Vector Machine (SVM) methods, and these classifiers weighted with spatial context using the Markov random field (MRF) model, were also introduced to provide a benchmark comparison with the MPk-NN method. The proposed approach increased classification accuracy significantly relative to the alternatives, and it is, thus, recommended for the identification of land-cover types with complex and diverse spatial distributions.     

A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric

Though the k-nearest neighbor (k-NN) pattern classifier is an effective learning algorithm, it can result in large model sizes. To compensate, a number of variant algorithms have been developed that condense the model size of the k-NN classifier at the expense of accuracy. To increase the accuracy of these condensed models, we present a direct boosting algorithm for the k-NN classifier that creates an ensemble of models with locally modified distance weighting. An empirical study conducted on 10 standard databases from the UCI repository shows that this new Boosted k-NN algorithm has increased generalization accuracy in the majority of the datasets and never performs worse than standard k-NN.     

用于多标记学习的局部顺序分类器链算法

标记间的相关性在分类问题中具有重要作用,目前有研究将标记相关性引入多标记学习,通过分类器链的形式将标记结果引入属性空间,为学习其他标记提供有用信息。分类器链中标记的预测顺序具有随机性,分类结果存在着很大的不确定性与不稳定性,且容易造成错误信息的传播。为此充分考虑标记的局部分布特性,提出了一种局部顺序分类器链算法,解决分类器链中分类器顺序问题。实验表明,该算法性能优于其他常用多标记学习算法。     

Cross-datasets facial expression recognition via distance metric learning and teacher-student model

Multimedia Tools and Applications - Large-scale high-quality datasets are a particularly important condition for facial expression recognition(FER) in the era of deep learning, but most of the...     

Prototype-based classifier learning for long-tailed visual recognition

Science China Information Sciences -     

Compositional metric learning for multi-label classification

Multi-label classification aims to assign a set of proper labels for each instance, where distance metric learning can help improve the generalization ability of instance-based multi-label classification models. Existing multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature space. In this paper, a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label space. On one hand, compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on component bases. On the other hand, compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label spaces. Due to the compositional nature of employed distance metric, the resulting problem admits quadratic programming formulation with linear optimization complexity w.r.t. the number of training examples.We also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional metric. Extensive experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.     

整合全局—局部度量学习的人体目标再识别

目的 人体目标再识别的任务是匹配不同摄像机在不同时间、地点拍摄的人体目标。受光照条件、背景、遮挡、视角和姿态等因素影响,不同摄相机下的同一目标表观差异较大。目前研究主要集中在特征表示和度量学习两方面。很多度量学习方法在人体目标再识别问题上了取得了较好的效果,但对于多样化的数据集,单一的全局度量很难适应差异化的特征。对此,有研究者提出了局部度量学习,但这些方法通常需要求解复杂的凸优化问题,计算繁琐。方法 利用局部度量学习思想,结合近几年提出的XQDA（cross-view quadratic discriminant analysis）和MLAPG（metric learning by accelerated proximal gradient）等全局度量学习方法,提出了一种整合全局和局部度量学习框架。利用高斯混合模型对训练样本进行聚类,在每个聚类内分别进行局部度量学习;同时在全部训练样本集上进行全局度量学习。对于测试样本,根据样本在高斯混合模型各个成分下的后验概率将局部和全局度量矩阵加权结合,作为衡量相似性的依据。特别地,对于MLAPG算法,利用样本在各个高斯成分下的后验概率,改进目标损失函数中不同样本的损失权重,进一步提高该方法的性能。结果 在VIPeR、PRID 450S和QMUL GRID数据集上的实验结果验证了提出的整合全局—局部度量学习方法的有效性。相比于XQDA和MLAPG等全局方法,在VIPeR数据集上的匹配准确率提高2.0%左右,在其他数据集上的性能也有不同程度的提高。另外,利用不同的特征表示对提出的方法进行实验验证,相比于全局方法,匹配准确率提高1.3%~3.4%左右。结论 有效地整合了全局和局部度量学习方法,既能对多种全局度量学习算法的性能做出改进,又能避免局部度量学习算法复杂的计算过程。实验结果表明,对于使用不同的特征表示,提出的整合全局—局部度量学习框架均可对全局度量学习方法做出改进。     

极限学习机的分类器集成模型研究

将极限学习机算法与旋转森林算法相结合,提出了以ELM算法为基分类器并以旋转森林算法为框架的RF-ELM集成学习模型。在8个数据集上进行了3组预测实验,根据实验结果讨论了ELM算法中隐含层神经元个数对预测结果的影响以及单个ELM模型预测结果不稳定的缺陷;将RF-ELM模型与单ELM模型和基于Bagging算法集成的ELM模型相比较,由稳定性和预测精度的两组对比实验的实验结果表明,对ELM的集成学习可以有效地提高ELM模型的性能,且RF-ELM模型较其他两个模型具有更好的稳定性和更高的准确率,验证了RF-ELM是一种有效的ELM集成学习模型。     

Action prediction via deep residual feature learning and weighted loss

Multimedia Tools and Applications - Action prediction based on partially observed videos is challenging as the information provided by partial videos is not discriminative enough for...     

A modified weighted tchebycheff metric for multiple objective programming

We show how to determine nondominated criterion vectors (and then nondominated solutions, if necessary) by a modified weighted Tchebycheff metric in cases when sets of all criterion vectors are finite or polyhedral.     

通道稳定性加权补充学习的实时视觉跟踪算法

为解决补充学习（Staple）跟踪算法在平面内旋转、部分遮挡时存在的跟踪失败问题,提出了一种通过通道稳定性加权的补充学习（CSStaple）跟踪算法。首先,使用标准相关滤波分类器检测出每层通道的响应值;然后,计算获得每层通道的稳定性权重,并乘到每层权重上,获得相关滤波响应;最后,通过融合颜色补充学习器的响应,得到最终的响应结果,响应中的最大值的位置即为跟踪结果。将所提算法与层和空间可靠性判别相关滤波（CSR-DCF）跟踪、对冲深度跟踪（HDT）、核化相关滤波（KCF）跟踪和Staple等跟踪算法进行了对比实验。实验结果表明,所提算法在成功率上表现最优,在OTB50和OTB100上比Staple分别高出2.5个百分点和0.9个百分点,验证了所提算法对目标在平面内旋转和部分遮挡时的有效性。     

Sparse semantic metric learning for image retrieval

Typical content-based image retrieval solutions usually cannot achieve satisfactory performance due to the semantic gap challenge. With the popularity of social media applications, large amounts of social images associated with user tagging information are available, which can be leveraged to boost image retrieval. In this paper, we propose a sparse semantic metric learning (SSML) algorithm by discovering knowledge from these social media resources, and apply the learned metric to search relevant images for users. Different from the traditional metric learning approaches that use similar or dissimilar constraints over a homogeneous visual space, the proposed method exploits heterogeneous information from two views of images and formulates the learning problem with the following principles. The semantic structure in the text space is expected to be preserved for the transformed space. To prevent overfitting the noisy, incomplete, or subjective tagging information of images, we expect that the mapping space by the learned metric does not deviate from the original visual space. In addition, the metric is straightforward constrained to be row-wise sparse with the ?_2,1-norm to suppress certain noisy or redundant visual feature dimensions. We present an iterative algorithm with proved convergence to solve the optimization problem. With the learned metric for image retrieval, we conduct extensive experiments on a real-world dataset and validate the effectiveness of our approach compared with other related work.     

Evaluation of a machine learning classifier for metamodels

Modeling is a ubiquitous activity in the process of software development. In recent years, such an activity has reached a high degree of intricacy, guided by the heterogeneity of the components, data sources, and tasks. The democratized use of models has led to the necessity for suitable machinery for mining modeling repositories. Among others, the classification of metamodels into independent categories facilitates personalized searches by boosting the visibility of metamodels. Nevertheless, the manual classification of metamodels is not only a tedious but also an error-prone task. According to our observation, misclassification is the norm which leads to a reduction in reachability as well as reusability of metamodels. Handling such complexity requires suitable tooling to leverage raw data into practical knowledge that can help modelers with their daily tasks. In our previous work, we proposed AURORA as a machine learning classifier for metamodel repositories. In this paper, we present a thorough evaluation of the system by taking into consideration different settings as well as evaluation metrics. More importantly, we improve the original AURORA tool by changing its internal design. Experimental results demonstrate that the proposed amendment is beneficial to the classification of metamodels. We also compared our approach with two baseline algorithms, namely gradient boosted decision tree and support vector machines. Eventually, we see that AURORA outperforms the baselines with respect to various quality metrics.

     

An approach to active learning for classifier systems

In this paper,the active learning mechanism is proposed to be used in classifier systems to cope with complex problems:an intelligent agent leaves its own signals in the environment and later collects and employs them to assist its learning process.Principles and components of the mechanism are outlined,followed by the introduction of its preliminary implementation in an actual system.An experiment wit te system in a dynamic problem is then introduced,together with discussions over its results.The paper is concluded by pointing out some possible improvements that can be made to the proposed framework.