Wenping Ma, Feifei Ti, Congling Li and Licheng Jiao
The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation.
Abstract
Purpose
The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation.
Design/methodology/approach
DICCA combines immune clone selection and differential evolution, and two populations are used in the evolutionary process. Clone reproduction and selection, differential mutation, crossover and selection are adopted to evolve two populations, which can increase population diversity and avoid local optimum. After extracting the texture features of an image and encoding them with real numbers, DICCA is used to partition these features, and the final segmentation result is obtained.
Findings
This approach is applied to segment all sorts of images into homogeneous regions, including artificial synthetic texture images, natural images and remote sensing images, and the experimental results show the effectiveness of the proposed algorithm.
Originality/value
The method presented in this paper represents a new approach to solving clustering problems. The novel method applies the idea two populations are used in the evolutionary process. The proposed clustering algorithm is shown to be effective in solving image segmentation.
Details
Keywords
B. Anthony Billings, Buagu N. Musazi, William H. Volz and Deborah K. Jones
This chapter evaluates the effectiveness of states' research and development (R&D, used to represent creditable research expenses) tax credits. Prior studies report mixed results…
Abstract
This chapter evaluates the effectiveness of states' research and development (R&D, used to represent creditable research expenses) tax credits. Prior studies report mixed results on the effect of state R&D tax credit incentives. Generally, such studies consider the influence of state R&D tax credits by applying the statutory income tax and R&D credit tax rates. We reexamine the effect of a state's entire tax burden instead of the statutory tax rates in moderating the effectiveness of a state's R&D tax credit incentives. After controlling for several nontax factors, such as the workplace environment, political environment, and workforce education levels in a regression analysis during the 2010–2013 period in 50 states, we find that statewide private-sector R&D spending is a positive function of the R&D tax credit and this effect increases with the overall level of the state tax burden. We attribute this finding to the fact that high tax burdens increase the present value of the R&D tax credits.