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The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation.

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.

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.

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.

This paper aims to solve the disadvantages of content-based domain ontology (CBDO) and metadata-based domain ontology (MDO) and improve organization and discovery efficiency of library resources by resource ontology (RO).

The paper constructed an RO model. Methods of informetrics are utilized to reveal semantic relationships among library resources. Methods of ontology, ontology-relational database mapping (O-R mapping) and relational database modelling are utilized to construct RO. Take author co-occurrence for example, the paper demonstrated the capability of RO model.

RO not only revealed the deep-level semantic relationships of metadata of library resources but also realized totally computer-automated processing. RO improved the efficiency of knowledge organization and discovery.

Semantic relationships revealed by RO are limited to simple metadata, which makes it difficult to reveal fine-grained semantic relationships. Ongoing research focuses on the revelation of semantic relationships based on the title and abstract.

The paper includes implications for utilizing methods of Informetrics to construct ontology.

This paper proposed a standardized process of ontology construction in library resources. It may be of potential interest for anyone who needs to effectively organize library resources.