Unsupervised event detection and classification of multichannel signals

In this paper, we present a new unsupervised method to classify a set of Multichanel Signals (MC) with unknown events. Each signal is characterized by a sequence of events where the number of events, the start time and the duration between events can change randomly. The proposed method helps in the classification and event detection of the MC signals by an expert which usually becomes a tedious and difficult task. To this end, first, the problem of classification of MC signals characterized by a succession of events is analyzed by transforming the MC signals into a set of temporal sequences of easy interpretation. The algorithm detects events by means of an optimal unsupervised classification. It is not necessary to know the nature of the events and formulate hypotheses regarding their behavior. Then, a set of multichannel electromyographic (EMG) signals with events is generated. These MC signals are used to test the proposed method.     

Unsupervised scene segmentation using sparse coding context

This paper presents an approach to image understanding on the aspect of unsupervised scene segmentation. With the goal of image understanding in mind, we consider ‘unsupervised scene segmentation’ a task of dividing a given image into semantically meaningful regions without using annotation or other human-labeled information. We seek to investigate how well an algorithm can achieve at partitioning an image with limited human-involved learning procedures. Specifically, we are interested in developing an unsupervised segmentation algorithm that only relies on the contextual prior learned from a set of images. Our algorithm incorporates a small set of images that are similar to the input image in their scene structures. We use the sparse coding technique to analyze the appearance of this set of images; the effectiveness of sparse coding allows us to derive a priori the context of the scene from the set of images. Gaussian mixture models can then be constructed for different parts of the input image based on the sparse-coding contextual prior, and can be combined into an Markov-random-field-based segmentation process. The experimental results show that our unsupervised segmentation algorithm is able to partition an image into semantic regions, such as buildings, roads, trees, and skies, without using human-annotated information. The semantic regions generated by our algorithm can be useful, as pre-processed inputs for subsequent classification-based labeling algorithms, in achieving automatic scene annotation and scene parsing.     

Unsupervised detection of acoustic events using information bottleneck principle

An unsupervised approach based on Information Bottleneck (IB) principle is proposed for detecting acoustic events from audio streams. In this paper, the IB principle is first concisely presented, and then the practical issues related to the application of IB principle to acoustic event detection are described in detail, including definitions of various variables, criterion for determining the number of acoustic events, tradeoff between amount of information preserved and compression of the initial representation, and detection steps. Further, we compare the proposed approach with both unsupervised and supervised approaches on four different types of audio files. Experimental results show that the proposed approach obtains lower detection errors and higher running speed compared to two state-of-the-art unsupervised approaches, and is little inferior to the state-of-the-art supervised approach in terms of both detection errors and runtime. The advantage of the proposed unsupervised approach over the supervised approach is that it does not need to pre-train classifiers and pre-know any prior information about audio streams.     

Unsupervised detection and localization of structural textures using projection profiles

The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. First, multi-scale isotropic filters are used to enhance the potential texton locations. Then, regularity of the textons is quantified in terms of the periodicity of projection profiles of filter responses within sliding windows at multiple orientations. Next, a regularity index is computed for each pixel as the maximum regularity score together with its orientation and scale. Finally, thresholding of this regularity index produces accurate localization of structural textures in images containing different kinds of textures as well as non-textured areas. Experiments using three different data sets show the effectiveness of the proposed method in complex scenes.     

Unsupervised minor prototype detection using an adaptive population partitioning algorithm

This paper presents a new partitioning algorithm, designated as the Adaptive C-Populations (ACP) clustering algorithm, capable of identifying natural subgroups and influential minor prototypes in an unlabeled dataset. In contrast to traditional Fuzzy C-Means clustering algorithms, which partition the whole dataset equally, adaptive clustering algorithms, such as that presented in this study, identify the natural subgroups in unlabeled datasets. In this paper, data points within a small, dense region located at a relatively large distance from any of the major cluster centers are considered to form a minor prototype. The aim of ACP is to adaptively separate these isolated minor clusters from the major clusters in the dataset. The study commences by introducing the mathematical model of the proposed ACP algorithm and demonstrates its convergence to a stable solution. The ability of ACP to detect minor prototypes is confirmed via its application to the clustering of three different datasets with different sizes and characteristics.     

Unsupervised and non-parametric learning-based anomaly detection system using vibration sensor data

Multimedia Tools and Applications - In this paper, we propose an anomaly detection system of machines using a hybrid learning mechanism that combines two kinds of machine learning approaches,...     

Emotion recognition from physiological signals using wireless sensors for presence technologies

In this article we describe a new approach to enhance presence technologies. First, we discuss the strong relationship between cognitive processes and emotions and how human physiology is uniquely affected when experiencing each emotion. Secondly, we introduce our prototype multimodal affective user interface. In the remainder of the paper we describe the emotion elicitation experiment we designed and conducted and the algorithms we implemented to analyse the physiological signals associated with emotions. These algorithms can then be used to recognise the affective states of users from physiological data collected via non-invasive technologies. The affective intelligent user interfaces we plan to create will adapt to user affect dynamically in the current context, thus providing enhanced social presence.     

胶囊内窥镜冗余图像数据自动筛除方法

针对胶囊内窥镜检查的海量图像数据,提出基于归一化互信息量及归一化互相关系数的冗余图像数据筛除方法。将图像在HSV色彩空间量化聚类;然后计算相邻图像的相似度系数,最后根据相似筛除比例进行迭代筛除。针对49例病例,按照70%的筛除比率,实验结果得到100%的病灶数量保留率和较低的图像误删率。基于归一化互信息量冗余图像数据筛除方法能够高效准确地筛除冗余图像数据并极大地缩短阅片时间;在该算法的基础上,开发了胶囊内窥镜图像自动筛查系统,为医生判诊提供辅助和支持。     

Unsupervised visit detection in smart homes

Assistive technologies for elderly often use ambient sensor systems to infer activities of daily living (ADL). In general such systems assume that only a single person (the resident) is present in the home. However, in real world environments, it is common to have visits and it is crucial to know when the resident is alone or not. We deal with this challenge by presenting a novel method that models regular activity patterns and detects visits. Our method is based on the Markov modulated Poisson process (MMPP), but is extended to allow the incorporation of multiple feature streams. The results from the experiments on nine months of sensor data collected in two apartments show that our model significantly outperforms the standard MMPP. We validate the generalisation of the model using two new data sets collected from an other sensor network.     

基于无监督Hebb规则的入侵检测方法

通过分析现有的入侵检测方法,提出了一个基于无监督Hebb规则的入侵检测方法。此方法采用高效的抓包工具抓取计算机网络数据包;根据抓取到的网络数据包的信息定义行为变量;根据无监督的Hebb规则构建网络行为模型;采用Hamming距离进行检测。实验证明该方法能够正确地构造网络行为模型,并能准确地检测出异常行为。     

基于特征选择的无监督入侵检测方法

为提高入侵检测系统的检测速度和效果,结合遗传算法提出了一种基于特征选择的无监督入侵检测方法。一方面利用改进的遗传算法作为搜索策略;一方面使用K均值聚类算法对提取特征后的数据进行聚类,并将类间离散度和类内离散度的相关比值作为特征子集的评价指标,从而实现最优特征子集的求解并用于无监督的入侵检测。实验结果表明,该方法由于解决了入侵检测的特征选择问题,与未采用特征选择的无监督入侵检测相比具有更好的性能。     

Unsupervised anomaly detection in unmanned aerial vehicles

A real-time anomaly detection solution indicates a continuous stream of operational and labelled data that must satisfy several resources and latency requirements. Traditional solutions to the problem rely heavily on well-defined features and prior supervised knowledge, where most techniques refer to hand-crafted rules derived from known conditions. While successful in controlled situations, these rules assume that good data is available for them to detect anomalies; indicating that these rules will fail to generalise beyond known scenarios.To investigate these issues, current literature is examined for solutions that can be used to detect known and unknown anomalous instances whilst functioning as an out-of-the-box approach for efficient decision-making. The applicability of the isolation forest is discussed for engineering applications using the Aero-Propulsion System Simulation dataset as a benchmark where it is shown to outperform other unsupervised distance-based approaches. Also, the authors have carried out real-time experiments on an unmanned aerial vehicle to highlight further applications of the method. Finally, some conclusions are drawn concerning its simplicity and robustness in handling diagnostic problems.     

Random eye state change detection in real-time using EEG signals

Maximum and minimum computed across channels is used to monitor the Electroencephalogram signals for possible change of the eye state. Upon detection of a possible change, the last two seconds of the signal is passed through Multivariate Empirical Mode Decomposition and relevant features are extracted. The features are then fed into Logistic Regression and Artificial Neural Network classifiers to confirm the eye state change. The proposed algorithm detects the eye state change with 88.2% accuracy in less than two seconds. This provides a valuable improvement in comparison to a recent procedure that takes about 20 minutes to classify new instances with 97.3% accuracy. The introduced algorithm is promising in the real-time eye state classification as increasing the training examples would increase its accuracy.     

Unsupervised change detection in SAR images using curvelet and L1-norm based soft segmentation

In this article, we propose a novel unsupervised change detection method for synthetic aperture radar (SAR) images. First, we generate a difference image as a weighted average of a log-ratio image and a mean-ratio image, which has the advantage of enhancing the information of changed regions and restraining the information of unchanged background regions simultaneously. Second, we propose a variational soft segmentation model based on non-differentiable curvelet regularization and L1-norm fidelity. Numerically, by using the split Bregman technique for curvelet regularization term and reformulating the L1-norm fidelity as weighted L2-norm fidelity, we get an effective algorithm in which each sub-problem has a closed-form solution. The numerical experiments and comparisons with several existing methods show that the proposed method is promising, with not only high robustness to non-Gaussian noise or outliers but also high change detection accuracy. Moreover, the proposed method is good at detecting fine-structured change areas. Especially, it outperforms other methods in preserving edge continuity and detecting curve-shaped changed areas.     

Unsupervised change detection using spectral features and a texture difference measure for VHR remote-sensing images

This article proposes an unsupervised change-detection method using spectral and texture information for very-high-resolution (VHR) remote-sensing images. First, a new local-similarity-based texture difference measure (LSTDM) is defined using a grey-level co-occurrence matrix. A mathematical analysis shows that LSTDM is robust with respect to noise and spectral similarity. Second, the difference image is generated by integrating the spectral and texture features. Then, the unsupervised change-detection problem in VHR remote-sensing images is formulated as minimizing an energy function related with changed and unchanged classes in the difference image. A modified expectation-maximization-based active contour model (EMCVM) is applied to the difference image to separate the changed and unchanged regions. Finally, two different experiments are performed with SPOT-5 images and compared with state-of-the-art unsupervised change-detection methods to evaluate the effectiveness of the proposed method. The results indicate that the proposed method can sufficiently increase the robustness with respect to noise and spectral similarity and obtain the highest accuracy among the methods addressed in this article.     

Unsupervised manifold alignment using soft-assign technique

In this paper, we propose a robust unsupervised algorithm for automatic alignment of two manifolds in different datasets with possibly different dimensionalities. The significant contribution is that the proposed alignment algorithm is performed automatically without any assumptions on the correspondences between the two manifolds. For such purpose, we first automatically extract local feature histograms at each point of the manifolds and establish an initial similarity between the two datasets by matching their histogram-based features. Based on such similarity, an embedding space is estimated where the distance between the two manifolds is minimized while maximally retaining the original structure of the manifolds. The elegance of this idea is that such complicated problem is formulated as a generalized eigenvalue problem, which can be easily solved. The alignment process is achieved by iteratively increasing the sparsity of correspondence matrix until the two manifolds are correctly aligned and consequently one can reveal their joint structure. We demonstrate the effectiveness of our algorithm on different datasets by aligning protein structures, 3D face models and facial images of different subjects under pose and lighting variations. Finally, we also compare with a state-of-the-art algorithm and the results show the superiority of the proposed manifold alignment in terms of vision effect and numerical accuracy.     

Unsupervised feature selection using feature similarity

In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure     

Unsupervised change detection using a novel fuzzy c-means clustering simultaneously incorporating local and global information

Multimedia Tools and Applications - This paper presents a novel fuzzy c-means (FCM) clustering simultaneously incorporating local and global information (FLGICM) method to unsupervised change...