Showing papers by "Wentao Fan published in 2021"

Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

Abstract: Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

Nonparametric variational learning of multivariate beta mixture models in medical applications

Abstract: Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering method “variational learning of infinite multivariate Beta mixture models.” The motivation behind proposing this technique is the flexibility of mixture models to fit the data. This approach has the capability to infer the model complexity and estimate model parameters from the observed data automatically. Moreover, as a label‐free method, it could also address the problem of high costs of medical data labeling, which can be undertaken just by medical experts. The performance of the model is evaluated on real medical applications and compared with other similar alternatives. We demonstrate the ability of our proposed method to outperform widely used methods in the field as it has been shown in experimental results.

Batch and online variational learning of hierarchical Dirichlet process mixtures of multivariate Beta distributions in medical applications

Abstract: Thanks to the significant developments in healthcare industries, various types of medical data are generated. Analysing such valuable resources aid healthcare experts to understand the illnesses more precisely and provide better clinical services. Machine learning as one of the capable tools could assist healthcare experts in achieving expressive interpretation and making proper decisions. As annotation of medical data is a costly and sensitive task that can be performed just by healthcare professionals, label-free methods could be significantly promising. Interpretability and evidence-based decision are other concerns in medicine. These needs were our motivators to propose a novel clustering method based on hierarchical Dirichlet process mixtures of multivariate Beta distributions. To learn it, we applied batch and online variational methods for finding the proper number of clusters as well as estimating model parameters at the same time. The effectiveness of the proposed models is evaluated on three medical real applications, namely oropharyngeal carcinoma diagnosis, osteosarcoma analysis, and white blood cell counting.

Clustering-Based Online News Topic Detection and Tracking Through Hierarchical Bayesian Nonparametric Models

Abstract: In this paper, we propose a clustering-based online news topic detection and tracking (TDT) approach based on hierarchical Bayesian nonparametric framework that allows topics to be shared across different news stories in a corpus. Our approach is formulated using the hierarchical Pitman-Yor process mixture model with the inverted Beta-Liouville (IBL) distribution as its component density, which has shown superior performance in modeling text data than the widely used Gaussian distribution. Moreover, we theoretically develop a convergence-guaranteed online learning algorithm that can effectively learn the proposed TDT model from a stream of news stories based on varational Bayes. The merits of our TDT approach are illustrated by comparing it with other well-defined clustering-based TDT approaches on different news data sets.

Combined Improved Dirichlet Models and Deep Learning Models for Road Extraction from Remote Sensing Images

Abstract: Combining Dirichlet Mixture Models (DMM) with deep learning models for road extraction is an attractive study topic. Benefiting from DMM, the manually labeling work is alleviated. However, DMM suff...

Unsupervised Learning Using Variational Inference on Finite Inverted Dirichlet Mixture Models with Component Splitting

Abstract: Unsupervised learning has been one of the essentials of pattern recognition and data mining. The role of Dirichlet family of mixture models in this field is inevitable. In this article, we propose a finite Inverted Dirichlet mixture model for unsupervised learning using variational inference. In particular, we develop an incremental algorithm with a component splitting approach for local model selection, which makes the clustering algorithm more efficient. We illustrate our model and learning algorithm with synthetic data and some real applications for occupancy estimation in smart homes and topic learning in images and videos. Extensive comparisons with comparable recent approaches have shown the merits of our proposed model.

Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model.

Abstract: Mixture models are a common unsupervised learning technique that have been widely used to statistically approximate and analyse heterogenous data. In this paper, an effective mixture model-based approach for positive vectors clustering and modeling is proposed. Our mixture model is based on the inverted Beta-Liouville (IBL) distribution. To deploy the proposed model, we introduce an entropy-based variational inference algorithm. The performance of the proposed model is evaluated on two real-world applications, namely, human activity recognition and image categorization.

Entropy-Based Variational Learning of Finite Generalized Inverted Dirichlet Mixture Model

Abstract: Mixture models are considered as a powerful approach for modeling complex data in an unsupervised manner. In this paper, we introduce a finite generalized inverted Dirichlet mixture model for semi-bounded data clustering, where we also developed a variational entropy-based method in order to flexibly estimate the parameters and select the number of components. Experiments on real-world applications including breast cancer detection and image categorization demonstrate the superior performance of our proposed model.