A hybrid approach using color spatial variance and novel object position prior for salient object detection

The expectation maximisation (EM) algorithm is an iterative maximum likelihood procedure often used for estimating the parameters of a mixture model. Theoretically, increases in the likelihood function are guaranteed as the algorithm iteratively improves upon previously derived parameter estimates. The algorithm is considered to converge when all parameter estimates become stable and no further improvements can be made to the likelihood value. However, to reduce computational time, it is often common practice for the algorithm to be stopped before complete convergence using heuristic approaches. In this paper, we consider various stopping criteria and evaluate their effect on fitting Gaussian mixture models (GMMs) to patient length of stay (LOS) data. Although the GMM can be successfully fitted to positively skewed data such as LOS, the fitting procedure often requires many iterations of the EM algorithm. To our knowledge, no previous study has evaluated the effect of different stopping criteria on fitting GMMs to skewed distributions. Hence, the aim of this paper is to evaluate the effect of various stopping criteria in order to select and justify their use within a patient spell classification methodology. Results illustrate that criteria based on the difference in the likelihood value and on the GMM parameters may not always be a good indicator for stopping the algorithm. In fact we show that the values of the difference in the variance parameters should be used instead, as these parameters are the last to stabilise. In addition, we also specify threshold values for the other stopping criteria.

The accurate modeling of uncertain variables, such as wind power and load uncertainty, is an essential foundation for the planning operation and scheduling decisions in power systems. Currently, Gaussian mixture model (GMM) and Dirichlet process mixture model (DPMM) are the primary techniques used to characterize uncertainties in power systems, which are commonly solved by expectation-maximization (EM) algorithm. However, for the massive data of uncertain variables, the algorithm encounters challenges in accurately obtaining GMM and DPMM with a lower time consumption. To address this issue, we propose a method for GMM uncertainty modeling in power systems considering the mutual assistance of latent variables. Specifically, the GMM of uncertain variables is first constructed, and the conditional probability is employed to characterize the mutual assistance of latent variables. Then, an improved EM algorithm is developed to obtain the optimal GMM parameters, in which the expectation step (E-step) and maximization step (M-step) of the algorithm are revised using the conditional probability. Importantly, the closed-form solutions for GMM parameters are rederived in the revised E-step and M-step. Finally, the proposed uncertainty modeling method is compared with the traditional GMM and DPMM on actual wind power and load data from Australia. The proposed method performs the efficiency and accuracy in characterizing uncertainty.

In a distribution power network, the load model has no certain pattern or predicted behaviour due to large range of data and changes in energy consumption for end-user consumers. Thus, a powerful analysis based on probabilistic structure is required. For this paper Gaussian Mixture Model (GMM) has been used. GMM is a powerful probability model that allows different types of load distributions to be presented as a combination of several Gaussian distributions. The parameters of GMM is unknown for large random data such as real load data and these parameters can be identified by Expectation Maximization (EM) algorithm. This paper presents a method to evaluate probabilistic load data concerning the time-evolution of any type of distribution load for any duration of time. The proposed method is explained through generated load data of 100 residential houses for duration of one year.

In this paper, the Gaussian mixture model (GMM) is introduced to implement channel multipath clustering. The GMM incorporates the covariance structure and the mean information of the channel multipaths, thus it can effectively reveal the similarity of the channel multipaths. First, the expectation-maximization (EM) algorithm is utilized to search for the posterior estimation of the GMM parameters. Then, the variational Bayesian (VB) algorithm is employed to optimize the GMM parameters to enhance the searching ability of EM and further to determine the optimal number of Gaussian distributions without resorting to cross-validation. Finally, a compact index (CI) is proposed to validate the clustering results reasonably. Thanks to the proposed CI, it is possible to find a close relationship among the GMM clustering mechanism, the multipath propagation characteristics and the CI evaluation index. Experiments with synthetic data and outdoor-to-indoor (O2I) channel data are presented to demonstrate the effectiveness of the proposed method.

Gaussian-input Gaussian mixture model for representing density maps and atomic models.

We present a novel approach for prediction of satellite image frame that uses a fuzzy rule based framework. The input-output membership functions for the premise and consequent parts of the rules are derived using a Gaussian Mixture Model (GMM). The weights of the fuzzy rules are represented as the prior probabilities of the respective Gaussian components. For obtaining the predictive fuzzy model, the GMM parameters are estimated via EM algorithm using a spatiotemporal representation of image sequence or video clips. Minimum Description Length (MDL) criterion is used to obtain a suitable predictive fuzzy model. The resulting model is successfully applied on a sequence of satellite images of tropical cyclone, Nargis, that made landfall in Myanmar on May 2, 2008. The quality of the predicted image is assessed using two criteria. The proposed approach is found to predict image frame successfully.

In this paper we apply generative modeling to Gaussian Mixture Models (GMM) as a solution to high dimensional novel event detection and analysis on radio frequency (RF) power generators. A family of Denoising and β-Disentangling Variational Autoencoders β-DVAE) is used to encode datasets into lower and more salient dimensions. The reduced feature sets are modeled using GMMs where model parameters are learned using the Expectation Maximization (EM) algorithm. The data is obtained from two different generator models operating under normal as well as known not-normal conditions. This approach is also tested on standard classification sets. Robust testing is reported to achieve a target class accuracy of 98.16% for the target RF generator. Additionally, the GMM parameters are decoded by the generative model to the original data space for calculating per-variable fitness in order to provide an initial novel event analysis for engineers and technicians. This per-variable fitness is very critical for determining each variable's contribution to a novelty so that engineers can perform informed trouble shooting and maintenance of the RF generators.

In this paper we seek a Gaussian mixture model (GMM) of an n-variate probability density function. Usually the parameters of GMMs are determined in the original n-dimensional space by optimizing a maximum likelihood (ML) criterion. A practical deficiency of this method of fitting GMMs is its poor performance when dealing with high-dimensional data since a large sample size is needed to match the accuracy that is possible in low dimensions. We propose a method for fitting the GMM based on the projection pursuit strategy. This GMM is highly constrained and hence its ability to model structure in subspaces is enhanced, compared to a direct ML fitting of a GMM in high dimensions. Our method is closely related to recently developed independent factor analysis (IFA) mixture models. The comparisons with ML fitting of GMM in n-dimensions and IFA mixtures show that the proposed method is an attractive choice for fitting GMMs using small sizes of training sets.

Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio

<p>SF1: Pimonidazole and carbonic anhydrase IX (CA-IX) staining patterns in 4T1 and MDA-MB-231 breast tumors; SF2: Breast tumor stained with hematoxylin & eosin (H&E), showing different stages of necrosis; SF3: Representative examples of the corresponding habitat maps from MRI and histology for the 4T1 and MDA-MB-231 tumor samples; SF4: Histograms showing the pixels values distribution of Area under the curve (AUC), slope and time to max (TTM) from DCE-MRI in each cluster of representative MRI habitat maps for 4T1 and MDA-MB-231 tumor samples; SF5: Representative examples showing that the DCE-MRI maps have visual similarity with pimonidazole staining in breast samples. SF6: Representative examples of MRI habitat maps, which were created by clustering five MRI-parameters [T2-map, T2*-map, Slope, Area under the curve (AUC) and time to max (TTM)] using a Gaussian Mixture Model (GMM) Expectation-Maximization algorithm. SF7: Comparison of mean values of each parameter from habitat maps generated by clustering five MRI-parameters [T2-map, T2*-map, Slope, Area under the curve (AUC) and time to max (TTM)]; SF8: Histograms showing the distribution of the Apparent diffusion coefficient (ADC) values in the cluster classified as non-viable tumor cells in the MRI habitat map. SF9: Representative images of the immunohistochemistry for perilipin-1 in breast tumor samples.</p>

The Expectation-Maximization algorithm is a very popular approach for estimating the parameters of Gaussian mixture models (GMMs). A known issue with GMM estimation is its sensitivity to outliers, which can lead to poor estimation performance depending on the dataset under consideration. A common approach to deal with this issue is robust estimation, which typically consists of reducing the influence of the outliers on the estimators by weighting the impact of some samples of the dataset considered as outliers. In an unsupervised context, it is difficult to know which sample from the database corresponds to a normal observation. To that extent, we propose to use within the EM algorithm an outlier detection step that attributes an anomaly score to each sample of the database in an unsupervised way. A modified Bayesian Information Criterion is also introduced to efficiently select the appropriate amount of outliers contained in a dataset. The proposed method is tested on a benchmark remote sensing dataset coming from the UCI Machine Learning Repository. The experimental results show the interest of the proposed robustification when compared to other benchmark imputation procedures.

Salient object detection is a challenging research field in computer vision. The existing saliency detection methods generally focus on finding feature maps for saliency computation. However, the combination of these feature maps significantly improves salient region(s) detection. In this paper, we propose a novel feature integration approach called U-FIN in which final saliency map is obtained by a weighted combination of individual feature maps. The proposed approach works in three phases viz. (i) artifact reference (AR) map generation (ii) weight learning and (iii) final saliency map computation. Firstly, AR map is produced using majority voting on the individual feature maps extracted from the input image. Secondly, linear regression is employed for weight learning which is used in the next phase. Finally, the individual feature maps are linearly combined using weights learned in the second phase to generate the final saliency map. Extensive experiments are conducted on two benchmark datasets, i.e., ASD and ECSSD to validate the proposed feature integration approach. The performance is measured in terms of precision, recall, receiver operating characteristic (ROC) curve, F-measure and area under the curve (AUC). Extensive experiments demonstrate the superiority of the proposed U-FIN approach against nine state-of-the-art saliency methods on ASD dataset and comparable on ECSSD dataset with the best performing methods.

Intelligent vehicles need reliable information about the environment in order to operate with total safety. In this paper we propose a flexible multi-module architecture for a multi-target detection and tracking system (MTDTS) complemented with a Bayesian object classification layer based on finite Gaussian mixture models (GMM). The GMM parameters are estimated by an expectation maximization (EM) algorithm, hence finite-component models were generated based on feature-vectors extracted from object's classes during the training stage. Using the joint mixture Gaussian pdf modelled for each class, a Bayesian approach is used to distinct the object's categories (persons, tree-trunks/posts, and cars) in a semi-structured outdoor environment based on data from a laser range finder (LRF). Experiments using real data scan confirm the robustness of the proposed architecture. This paper investigates a particular problem: detection, tracking and classification of objects in cybercars-like outdoor environments

Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC. Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve. A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (P>0.05). The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.

Unmixing urban hyperspectral imagery using probability distributions to represent endmember variability