artificial bee colony (ABC) algorithm is a powerful stochastic evolutionary algorithm, which is widely used to solve complex optimisation problems. However, ABC is good at exploration but poor at exploitation because ...
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artificial bee colony (ABC) algorithm is a powerful stochastic evolutionary algorithm, which is widely used to solve complex optimisation problems. However, ABC is good at exploration but poor at exploitation because of its search strategy. For overcoming the shortcomings of original ABC algorithm, such as slow convergence and low solution accuracy, we propose a new ABC algorithm - artificial bee colonyalgorithm with improved special centre (ISC-ABC). Firstly, an improved special centre is used to determine the current gbest position, and lead the colony convergence. Secondly, Employed bees incorporate the information of gbest solution into the search strategy. By this way, the new candidate solutions are always around with gbest. Finally, compare result on 12 classic functions. The results testify that ISC-ABC performs significantly better than original ABC and several recently proposed similar algorithm.
Content-Based Image Retrieval (CBIR) refers to techniques that retrieve images based on their content, as opposed to based on metadata. A CBIR system performs indexing and retrieval tasks using features like color, te...
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ISBN:
(纸本)9781479938346
Content-Based Image Retrieval (CBIR) refers to techniques that retrieve images based on their content, as opposed to based on metadata. A CBIR system performs indexing and retrieval tasks using features like color, texture and shape computed from images as opposed to using the whole images. In the medical field, content based image retrieval is used to aid radiologist to retrieve of images with similar contents. CBIR methods are usually developed for specific features of images, so that those methods are not readily applicable across different kinds of medical images. Content-Based Medical Image Retrieval (CBMIR) refers to techniques that retrieve images from medical image databases. A CBMIR system using the medical image features like Haralick features, Zernike moments, histogram intensity features and run-length features. In this study, CBMIR system With improved feature selection method is developed using a hybrid approach of "branch and bound algorithm" and "artificial bee colonyalgorithm" using the breast cancer, Brain tumor and thyroid images and classification is performed using Fuzzy based Relevance Vector Machine (FRVM) to form groups of relevant image features The Euclidean distance measurement is used to assess the similarity between query images and database images. A Relevance feedback method using diverse density algorithm is used to improve the performance of content-based medical image Retrieval. An improved feature selection method is used to reduces the existing system dimensionality curse problem and improve the performance of the system.
With the development of science and technology and the passage of time, the automatic layout of pipelines in buildings has been paid more and more attention. The development of building intelligence depends on the rea...
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