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Currently, image segmentation is widely used in face recognition, medical imaging, traffic control systems, and many other fields. The traditional possibilistic C-means (PCM) algorithm reduces the impact of outliers and noise on the computation of clustering centers; however, the clustering results are still poor due to high noise points. In this paper, an interval possibilistic C-means (IVPCM) algorithm is proposed that expands the natural number of image pixels to an interval value. In addition, a method to calculate the sample distance is proposed. Then, secondary feature extraction of interval values is conducted to improve the clustering. Several experiments show that IVPCM has more advantages in segmentation performance. [ABSTRACT FROM AUTHOR]

A method to enhance Web service clustering is proposed in this paper. Since current service clustering methods usually face low quality of service representation vectors and lack consideration of service collaboration, we try to provide an improved topic model to generate high-quality service representation vectors and design a service clustering method to integrate function similarity and collaboration similarity. First, by introducing feature word extraction and probability distribution correction into GSDMM, we present a model called TE-GSDMM (topic enhanced Gibbs sampling algorithm for the Dirichlet Multinomial Mixture model). Then, a service collaboration graph is put forward to model cooperation relationships and generate service collaboration vectors. Collaboration similarity is assessed by the similarity of service collaboration vectors. Finally, the K-means++ algorithm is employed to cluster Web services by evaluating service function similarity and collaboration similarity. Experiments show that TE-GSDMM outperforms other topic models in generating high-quality service representation vectors for service clustering. Moreover, service clustering performance is further improved by integrating collaboration similarity. Thus, the proposed method effectively enhances Web service clustering by improving the quality of service representation vectors and integrating service collaboration similarity. [ABSTRACT FROM AUTHOR]

It is widely accepted that webpage layout is important for website aesthetics. However, knowledge is limited about which kind of webpage layout is aesthetic. In this paper, we conduct a large-scale analysis of 13,017 real webpages with different aesthetic levels. The results demonstrate that there are significant differences between aesthetic webpages and non-aesthetic webpages in several aspects of layout design, such as the position of objects and the average number of objects. In order to identify whether the web layout is aesthetic, we propose a novel Webpage Layout Aesthetic Evaluation model (WLAE) based on an improved Adaboost algorithm to automatically predict the aesthetics of a webpage layout. Experimental results show that our WLAE model is significantly better than other existing methods. Based on the feature analysis of 13,017 real-world webpages and the contribution of each feature to webpage aesthetic prediction, we have summarized 4 webpage design principles for webpage designers. Besides, a large number of real webpages collected by our team can be used for further research on webpage layout aesthetic design and/or related research. [ABSTRACT FROM AUTHOR]

Recently, robust-norm distance related principal component analysis (PCA) for feature extraction has been shown to be very effective for image analysis, which considers either minimization of reconstruction error or maximization of data variance in low-dimensional subspace. However, both of them are important for feature extraction. Furthermore, most of existing methods cannot obtain satisfactory results due to the utilization of inflexible robust norm for distance metric. To address these problems, this paper proposes a novel robust PCA formulation called Double L 2,p -norm based PCA (DLPCA) for feature extraction, in which the minimization of reconstruction error and the maximization of variance are simultaneously taken into account in a unified framework. In the reconstruction error function, we target to learn a latent subspace to bridge the relationship between the transformed features and the original features. To guarantee the objective to be insensitive to outliers, we take L 2,p -norm as the distance metric for both reconstruction error and data variance. These characteristics make our method more applicable for feature extraction. We present an effective iterative algorithm to obtain the solution of this challenging work, and conduct theoretical analysis on the convergence of the algorithm. The experimental results on several databases show the effectiveness of our model. [ABSTRACT FROM AUTHOR]