Методы выявления однородных и неоднородных групп объектов на основе неопределенных качественных данных

In this paper, the criteria of detection of groups of objects are suggested. These criteria are based on uncertain estimates of objects qualitative attributes. The tasks of the homogeneous and heterogeneous detection of groups of objects are solved. In the homogeneous groups, the values of cognominal qualitative attributes are equal. In the heterogeneous groups, the values of such attributes may differ, but they have to match the a priori set valid combinations. The groups detection is based on the graph-theoretical approach. The decision about the pair of objects belonging to the same group is made by the ternary logic. That allows detecting possible and reliable object groups.В статье предложены критерии выявления групп объектов на основе не-определенных оценок значений их качественных признаков. Решаются задачи выявления однородных и неоднородных групп объектов. В однородных группах значения одноименных качественных признаков всех объектов совпадают. В неоднородных группах объектов значения таких признаков могут не совпадать, однако должны удовлетворять априорно заданным допустимым сочетаниям. Для выявления групп объектов применяется теоретико-графовый подход. При этом неопределенные оценки признаков объектов представляются в виде множеств их возможных значений. При принятии решения о принадлежности пары объектов к одной группе используется трехзначная логика, что позволяет выявлять возможные и достоверные группы

Multiple Classifier System for Remote Sensing Image Classification: A Review

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+). Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community

Non-invasive detection and assessment of coronary stenosis from blood mean residence times.

Coronary artery stenosis is an abnormal narrowing of a coronary artery caused by an atherosclerotic lesion that reduces lumen space. Fractional flow reserve (FFR) is the gold standard method to determine the severity of coronary stenosis based on the determination of rest and hyperemic pressure fields, but requires an invasive medical procedure. Normal FFR is 1.0 and FFR RT, to account for varying volume and flow rate of individual segments. BloodRT was computed in 100 patients who had undergone the pressure-wire FFR procedure, and a threshold for BloodRT was determined to assess the physiological significance of a stenosis, analogous to the diagnostic threshold for FFR. The threshold exhibited excellent discrimination in detecting significant from non-significant stenosis compared to the gold standard pressure-wire FFR, with sensitivity of 98% and specificity of 96%. When applied to clinical practice, this could potentially allow practicing cardiologists to accurately assess and quantify the severity of coronary stenosis without resorting to invasive catheter-based techniques. The first 100 patient study required a clinically determined blood flow rate as a key model input. To create a more non-invasive process, a multiple linear regression approach was employed to determine blood flow rate entering a given artery segment. To validate this method, BloodRT was computed for a new set of 100 patients using the regression derived blood flow rate. The sensitivity and specificity were 95% and 97%, respectively, indicating similar discrimination compared to the clinically derived flow rate. The method was also applied to a succession of stenosis in series. When BloodRT of each individual stenosis was well above the threshold for significance, the cumulative effect of all stenoses led to an overall BloodRT below the threshold of hemodynamic significance

Advanced Feature Learning and Representation in Image Processing for Anomaly Detection

Techniques for improving the information quality present in imagery for feature extraction are proposed in this thesis. Specifically, two methods are presented: soft feature extraction and improved Evolution-COnstructed (iECO) features. Soft features comprise the extraction of image-space knowledge by performing a per-pixel weighting based on an importance map. Through soft features, one is able to extract features relevant to identifying a given object versus its background. Next, the iECO features framework is presented. The iECO features framework uses evolutionary computation algorithms to learn an optimal series of image transforms, specific to a given feature descriptor, to best extract discriminative information. That is, a composition of image transforms are learned from training data to present a given feature descriptor with the best opportunity to extract its information for the application at hand. The proposed techniques are applied to an automatic explosive hazard detection application and significant results are achieved

A generic framework for context-dependent fusion with application to landmine detection.

For complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be a viable alternative to using a single classifier. Over the past few years, a variety of schemes have been proposed for combining multiple classifiers. Most of these were global as they assign a degree of worthiness to each classifier, that is averaged over the entire training data. This may not be the optimal way to combine the different experts since the behavior of each one may not be uniform over the different regions of the feature space. To overcome this issue, few local methods have been proposed in the last few years. Local fusion methods aim to adapt the classifiers\u27 worthiness to different regions of the feature space. First, they partition the input samples. Then, they identify the best classifier for each partition and designate it as the expert for that partition. Unfortunately, current local methods are either computationally expensive and/or perform these two tasks independently of each other. However, feature space partition and algorithm selection are not independent and their optimization should be simultaneous. In this dissertation, we introduce a new local fusion approach, called Context Extraction for Local Fusion (CELF). CELF was designed to adapt the fusion to different regions of the feature space. It takes advantage of the strength of the different experts and overcome their limitations. First, we describe the baseline CELF algorithm. We formulate a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. The context identification component thrives to partition the input feature space into different clusters (called contexts), while the fusion component thrives to learn the optimal fusion parameters within each cluster. Second, we propose several variations of CELF to deal with different applications scenario. In particular, we propose an extension that includes a feature discrimination component (CELF-FD). This version is advantageous when dealing with high dimensional feature spaces and/or when the number of features extracted by the individual algorithms varies significantly. CELF-CA is another extension of CELF that adds a regularization term to the objective function to introduce competition among the clusters and to find the optimal number of clusters in an unsupervised way. CELF-CA starts by partitioning the data into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose the competition gradually become depleted and vanish. Third, we propose CELF-M that generalizes CELF to support multiple classes data sets. The baseline CELF and its extensions were formulated to use linear aggregation to combine the output of the different algorithms within each context. For some applications, this can be too restrictive and non-linear fusion may be needed. To address this potential drawback, we propose two other variations of CELF that use non-linear aggregation. The first one is based on Neural Networks (CELF-NN) and the second one is based on Fuzzy Integrals (CELF-FI). The latter one has the desirable property of assigning weights to subsets of classifiers to take into account the interaction between them. To test a new signature using CELF (or its variants), each algorithm would extract its set of features and assigns a confidence value. Then, the features are used to identify the best context, and the fusion parameters of this context are used to fuse the individual confidence values. For each variation of CELF, we formulate an objective function, derive the necessary conditions to optimize it, and construct an iterative algorithm. Then we use examples to illustrate the behavior of the algorithm, compare it to global fusion, and highlight its advantages. We apply our proposed fusion methods to the problem of landmine detection. We use data collected using Ground Penetration Radar (GPR) and Wideband Electro -Magnetic Induction (WEMI) sensors. We show that CELF (and its variants) can identify meaningful and coherent contexts (e.g. mines of same type, mines buried at the same site, etc.) and that different expert algorithms can be identified for the different contexts. In addition to the land mine detection application, we apply our approaches to semantic video indexing, image database categorization, and phoneme recognition. In all applications, we compare the performance of CELF with standard fusion methods, and show that our approach outperforms all these methods