In general object detection, scale variation is always a big challenge. At present, feature pyramid networks are employed in numerous methods to alleviate the problems caused by large scale range of ...objects in object detection, which makes use of multi-level features extracted from the backbone for top-down upsampling and fusion to acquire a set of multi-scale depth image features. However, the feature pyramid network proposed by Ghiasi et al. adopts a simple fusion method, which fails to consider the fusion feature context, and therefore, it is difficult to acquire good features. In addition, the fusion of multi-scale features directly by traditional upsampling is prone to feature misalignment and loss of details. In this paper, an adaptive feature pyramid network is proposed based on the feature pyramid network to alleviate the foregoing potential problems, which includes two major designs, i.e., adaptive feature upsampling and adaptive feature fusion. The adaptive feature upsampling aims to predict a group of sampling points of each pixel through some models, and constitute feature representation of the pixel by feature combination of sampling points, while adaptive feature fusion is to construct pixel-level fusion weights between fusion features through attention mechanism. The experimental results verified the effectiveness of the method proposed in this paper. On the public object detection dataset MS-COCO test-dev, Faster R-CNN model achieved performance improvement of 1.2 AP by virtue of the adaptive feature pyramid network, and FCOS model could achieve performance improvement of 1.0 AP. What's more, the experiments also validated that the adaptive feature pyramid network proposed herein was more accurate for object localization.
Clustering is an unsupervised machine learning technique whose goal is to cluster unlabeled data. But traditional clustering methods only output a set of results and do not provide any explanations ...of the results. Although in the literature a number of methods based on decision tree have been proposed to explain the clustering results, most of them have some disadvantages, such as too many branches and too deep leaves, which lead to complex explanations and make it difficult for users to understand. In this paper, a hypercube overlay model based on multi-objective optimization is proposed to achieve succinct explanations of clustering results. The model designs two objective functions based on the number of hypercubes and the compactness of instances and then uses multi-objective optimization to find a set of nondominated solutions. Finally, an Utopia point is defined to determine the most suitable solution, in which each cluster can be covered by as few hypercubes as possible. Based on these hypercubes, an explanations of each cluster is provided. Upon verification on synthetic and real datasets respectively, it shows that the model can provide a concise and understandable explanations to users.
In the process of multi-exposure image fusion (MEF), the appearance of various distortions will inevitably cause the deterioration of visual quality. It is essential to predict the visual quality of ...MEF images. In this work, a novel blind image quality assessment (IQA) method is proposed for MEF images considering the detail, structure, and color characteristics. Specifically, to better perceive the detail and structure distortion, based on the joint bilateral filtering, the MEF image is decomposed into two layers (i.e., the energy layer and the structure layer). Obviously, this is a symmetric process that the two decomposition results can independently and almost completely describe the information of MEF images. As the former layer contains rich intensity information and the latter captures some image structures, some energy-related and structure-related features are extracted from these two layers to perceive the detail and structure distortion phenomena. Besides, some color-related features are also obtained to present the color degradation which are combined with the above energy-related and structure-related features for quality regression. Experimental results on the public MEF image database demonstrate that the proposed method achieves higher performance than the state-of-the-art quality assessment ones.
In the process of multi-exposure image fusion (MEF), the appearance of various distortions will inevitably cause the deterioration of visual quality. It is essential to predict the visual quality of ...MEF images. In this work, a novel blind image quality assessment (IQA) method is proposed for MEF images considering the detail, structure, and color characteristics. Specifically, to better perceive the detail and structure distortion, based on the joint bilateral filtering, the MEF image is decomposed into two layers (i.e., the energy layer and the structure layer). Obviously, this is a symmetric process that the two decomposition results can independently and almost completely describe the information of MEF images. As the former layer contains rich intensity information and the latter captures some image structures, some energy-related and structure-related features are extracted from these two layers to perceive the detail and structure distortion phenomena. Besides, some color-related features are also obtained to present the color degradation which are combined with the above energy-related and structure-related features for quality regression. Experimental results on the public MEF image database demonstrate that the proposed method achieves higher performance than the state-of-the-art quality assessment ones.
It is crucial to evaluate the quality of clustering results in cluster analysis. Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when ...dealing with non-spherical datasets. One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters. In this paper, a new robust distance measure, one into which density is incorporated, is designed to solve the problem, and an internal validity index based on this separation measure is then proposed. This index can cope with both the spherical and non-spherical structure of clusters. The experimental results indicate that the proposed index outperforms some classical CVIs.
Many clustering approaches have been proposed in the literature, but most of them are vulnerable to the different cluster sizes, shapes and densities. In this paper, we present a graph-theoretical ...clustering method which is robust to the difference. Based on the graph composed of two rounds of minimum spanning trees (MST), the proposed method (2-MSTClus) classifies cluster problems into two groups, i.e. separated cluster problems and touching cluster problems, and identifies the two groups of cluster problems automatically. It contains two clustering algorithms which deal with separated clusters and touching clusters in two phases, respectively. In the first phase, two round minimum spanning trees are employed to construct a graph and detect separated clusters which cover distance separated and density separated clusters. In the second phase, touching clusters, which are subgroups produced in the first phase, can be partitioned by comparing cuts, respectively, on the two round minimum spanning trees. The proposed method is robust to the varied cluster sizes, shapes and densities, and can discover the number of clusters. Experimental results on synthetic and real datasets demonstrate the performance of the proposed method.
This study aimed to obtain further in-depth information on the value of metagenomic next-generation sequencing (mNGS) for diagnosing pulmonary aspergillosis in non-neutropenic patients. We did a ...retrospective study, in which 33 non-neutropenic patients were included, of which 12 were patients with pulmonary aspergillosis and 21 were diagnosed with non-pulmonary aspergillosis. Fungi and all other co-pathogens in bronchoalveolar lavage fluid (BALF) (27 cases), blood (6 cases), and/or pleural fluid (1 case) samples were analyzed using mNGS. One of the patients submitted both BALF and blood samples. We analyzed the clinical characteristics, laboratory tests, and radiologic features of pulmonary aspergillosis patients and compared the diagnostic accuracy, including sensitivity, specificity, positive predictive value, and negative predictive value of mNGS with conventional etiological methods and serum (1,3)-β-D-glucan. We also explored the efficacy of mNGS in detecting mixed infections and co-pathogens. We further reviewed modifications of antimicrobial therapy for patients with pulmonary aspergillosis according to the mNGS results. Finally, we compared the detection of
Aspergillus
in BALF and blood samples from three patients using mNGS. In non-neutropenic patients, immunocompromised conditions of non-pulmonary aspergillosis were far less prevalent than in patients with pulmonary aspergillosis. More patients with pulmonary aspergillosis received long-term systemic corticosteroids (50% vs. 14.3%, p < 0.05). Additionally, mNGS managed to reach a sensitivity of 91.7% for diagnosing pulmonary aspergillosis, which was significantly higher than that of conventional etiological methods (33.3%) and serum (1,3)-β-D-glucan (33.3%). In addition, mNGS showed superior performance in discovering co-pathogens (84.6%) of pulmonary aspergillosis; bacteria, bacteria-fungi, and bacteria-PJP-virus were most commonly observed in non-neutropenic patients. Moreover, mNGS results can help guide effective treatments. According to the mNGS results, antimicrobial therapy was altered in 91.7% of patients with pulmonary aspergillosis. The diagnosis of
Aspergillus
detected in blood samples, which can be used as a supplement to BALF samples, seemed to show a higher specificity than that in BALF samples. mNGS is a useful and effective method for the diagnosis of pulmonary aspergillosis in non-neutropenic patients, detection of co-pathogens, and adjustment of antimicrobial treatment.
Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in ...various fields such as finance, industry, and medicine. To address the problem that the traditional linear regression model is difficult to handle uncertain data, we propose a granule-based elastic network regression model. First we construct granules and granular vectors by granulation methods. Then, we define multiple granular operation rules so that the model can effectively handle uncertain data. Further, the granular norm and the granular vector norm are defined to design the granular loss function and construct the granular elastic network regression model. After that, we conduct the derivative of the granular loss function and design the granular elastic network gradient descent optimization algorithm. Finally, we performed experiments on the UCI datasets to verify the validity of the granular elasticity network. We found that the granular elasticity network has the advantage of good fit compared with the traditional linear regression model.
A multi-exposure fused (MEF) image is generated by multiple images with different exposure levels, but the transformation process will inevitably introduce various distortions. Therefore, it is worth ...discussing how to evaluate the visual quality of MEF images. This paper proposes a new blind quality assessment method for MEF images by considering their characteristics, and it is dubbed as BMEFIQA. More specifically, multiple features that represent different image attributes are extracted to perceive the various distortions of MEF images. Among them, structural, naturalness, and colorfulness features are utilized to describe the phenomena of structure destruction, unnatural presentation, and color distortion, respectively. All the captured features constitute a final feature vector for quality regression via random forest. Experimental results on a publicly available database show the superiority of the proposed BMEFIQA method to several blind quality assessment methods.
The multi-exposure fusion (MEF) technique provides humans a new opportunity for natural scene representation, and the related quality assessment issues are urgent to be considered for validating the ...effectiveness of these techniques. In this paper, a curvature and entropy statistics-based blind MEF image quality assessment (CE-BMIQA) method is proposed to perceive the quality degradation objectively. The transformation process from multiple images with different exposure levels to the final MEF image leads to the loss of structure and detail information, so that the related curvature statistics features and entropy statistics features are utilized to portray the above distortion presentation. The former features are extracted from the histogram statistics of surface type map calculated by mean curvature and Gaussian curvature of MEF image. Moreover, contrast energy weighting is attached to consider the contrast variation of the MEF image. The latter features refer to spatial entropy and spectral entropy. All extracted features based on a multi-scale scheme are aggregated by training the quality regression model via random forest. Since the MEF image and its feature representation are spatially symmetric in physics, the final prediction quality is symmetric to and representative of the image distortion. Experimental results on a public MEF image database demonstrate that the proposed CE-BMIQA method achieves more outstanding performance than the state-of-the-art blind image quality assessment ones.
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