Phishing web image segmentation based on improving spectral clustering

Abstract

This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering. Firstly, we construct a set of points which are composed of spatial location pixels and gray levels from a given image. Secondly, the data is clustered in spectral space of the similar matrix of the set points, in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution, we introduce the clone operator, Cauthy mutation to enlarge the scale of clustering centers, quantum-inspired evolutionary algorithm to find the global optimal clustering centroids. Compared with phishing web image segmentation based on K-means, experimental results show that the segmentation performance of our method gains much improvement. Moreover, our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.

Investigation of gap filling ability of Ag–Cu–In brazing filler metals

“…Cadmium in silver brazing filler metals also has high vapor pressure which is unsuitable for vacuum brazing [6]. Therefore, many attempts have been made to solve the problem of cadmium fume and to produce cadmium-free alloys that have similar characteristics of high fluidity and low melting point as those of cadmium-bearing alloys [7][8][9][10][11][12][13][14][15][16]. However, most of alloys that have been developed contain zinc which is also a volatile material.…”