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Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images. [ABSTRACT FROM AUTHOR]

Superpixel-based segmentation has been widely used as a primary prepossessing step to simplify the subsequent image processing tasks. Since determining the number of clusters is subjective and varies based on the type of image, the segmentation algorithm may provide over-segmented or under-segmented superpixels. This paper proposes an image segmentation method to improve the SLIC superpixel by region merging. It aims to improve the segmentation accuracy without defining a precise number of superls. The color difference between superpixels is employed as a homogeneity criterion for the merging process. The Berkeley dataset is used with different quantitative performance metrics to evaluate the proposed model's performance. Results obtained from probabilistic rand index (PRI), boundary recall, and under-segmentation error proved the ability of the proposed algorithm to provide comparable segmentation with a reduced number of clusters. [ABSTRACT FROM AUTHOR]

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu's and Kapur's methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques were introduced for bi-level thresholding. However, these techniques can be expanded to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. To this end, various optimization techniques have been used to overcome this drawback. Recently, a new optimization algorithm called Battle Royal Optimizer (BRO) has been published, which is shown to solve various optimization tasks effectively. In this study, BRO has been applied to yield optimum threshold values in multilevel image thresholding. Here is also demonstrated the effectiveness of BRO for image segmentation on various images from the standard publicly accessible Berkeley segmentation dataset. We compare the performance of BRO to other state-of-the-art optimization-based methods and show that it outperforms them in terms of fitness value, Peak Signal-to-Noise Ratio, Structural Similarity Index Method, Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation. These results underscore the potential of BRO as a promising solution for image segmentation tasks, particularly through its effective implementation of multilevel thresholding. [ABSTRACT FROM AUTHOR]

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model. Although the Gaussian mixture model enhances the flexibility of image segmentation, it does not reflect spatial information and is sensitive to the segmentation parameter. In this study, we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model (GMM) without parameter estimation. The proposed model highlights the residual region with considerable information and constructs color saliency. Second, we incorporate the content-based color saliency as spatial information in the Gaussian mixture model. The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria. Finally, the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation. A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art, facilitating both analytical and aesthetic objectives. For experiments, we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset. In the study, the proposed model showcases notable advancements in unsupervised image segmentation, with probabilistic rand index (PRI) values reaching 0.80, BDE scores as low as 12.25 and 12.02, compactness variations at 0.59 and 0.7, and variation of information (VI) reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets, respectively, outperforming current leading-edge methods and yielding more precise segmentations. [ABSTRACT FROM AUTHOR]

While image segmentation is crucial in various computer vision applications, such as autonomous driving, grasping, and robot navigation, annotating all objects at the pixel-level for training is nearly impossible. Therefore, the study of unsupervised image segmentation methods is essential. In this paper, we present a pixel-level clustering framework for segmenting images into regions without using ground truth annotations. The proposed framework includes feature embedding modules with an attention mechanism, a feature statistics computing module, image reconstruction, and superpixel segmentation to achieve accurate unsupervised segmentation. Additionally, we propose a training strategy that utilizes intra-consistency within each superpixel, inter-similarity/dissimilarity between neighboring superpixels, and structural similarity between images. To avoid potential over-segmentation caused by superpixel-based losses, we also propose a post-processing method. Furthermore, we present an extension of the proposed method for unsupervised semantic segmentation. We conducted experiments on three publicly available datasets (Berkeley segmentation dataset, PASCAL VOC 2012 dataset, and COCO-Stuff dataset) to demonstrate the effectiveness of the proposed framework. The experimental results show that the proposed framework outperforms previous state-of-the-art methods. [Display omitted] [ABSTRACT FROM AUTHOR]

Around 1970, the City of Berkeley briefly became an epicenter of radical experimentation in urban planning and design, directly stemming from the counterculture of the late 1960s. This essay examines the ideological and political emergence of Berkeley's counterculture urbanism, arguing that its experiments left two important legacies in the history of planning. On the level of utopian thought, it articulated a clear alternative to mainstream capitalist urban development, or what Henri Lefebvre called "abstract space." On the level of contemporary planning practices, it opened up still-unresolved conflicts, especially between localized environmental preservation and the abstract, economic demands for affordable housing. [ABSTRACT FROM AUTHOR]

Compound heat wave (CompoundHW) has attracted extensive attention for its prolonged extreme heat from daytime to nighttime during its process. However, the performance of identifying and characterizing CompoundHW across different datasets has not been systematically evaluated. Here, we compared the similarities and differences of the ERA5, Berkeley Earth, CHIRTS and CPC datasets in identifying and characterizing CompoundHW. Results showed that the match of CompoundHW identification between datasets was consistent in both temporal and spatial dimensions, with the highest match observed between the ERA5 and CHIRTS datasets. Match of CompoundHW identification exhibited significant correlation with the density of observation stations, with matching rates above 50% in regions with dense observation networks, but extremely low match in regions with sparse data coverage. The rising trends of the CompoundHW metrics were captured by all datasets, especially in parts of North America, Europe, western Russia and Asia. Despite differences in the amplitude of CompoundHW changes across the four datasets, over 42% of global regions concurred on the changes in CompoundHW frequency, duration, and magnitude, and more than 27% agreed on the changes in the proportion of CompoundHW occurrences. Inconsistencies of CompoundHW changes were predominantly observed in regions with low matching rates, indicating that precise identification of CompoundHW is the basis for characterizing the changes in CompoudHW characteristics accurately. This study highlights the importance of multiple datasets comparison in heat wave research, especially in metrics defined by multiple climate variables and regions with sparse observational data. [ABSTRACT FROM AUTHOR]