How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance.To address this problem,this paper introduces a novel image retrieval mechanism based on the family fi...How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance.To address this problem,this paper introduces a novel image retrieval mechanism based on the family filtration in object region.First,we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object,retrieved from the corpus based on 100 images,as a result of the first rank.To further improve retrieval performance,we add an efficient spatial consistency stage,which is named family-based spatial consistency filtration,to re-rank the results returned by the first rank.We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of"TREC Video Retrieval Evaluation 2005(TRECVID2005)".The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality.The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.展开更多
为提高光伏功率预测的准确性和稳定性,提出了一种新颖的基于自适应区域搜索的改进多目标浣熊优化算法(multi-objective Coati optimization algorithm with adaptive region exploration,AMOCOA),用于提升短期光伏功率预测模型性能。提...为提高光伏功率预测的准确性和稳定性,提出了一种新颖的基于自适应区域搜索的改进多目标浣熊优化算法(multi-objective Coati optimization algorithm with adaptive region exploration,AMOCOA),用于提升短期光伏功率预测模型性能。提出种群自适应划分策略提升算法搜索能力,引入多项式变异算子,扩大搜索区域,通过围捕路径优化策略确保种群向最优解逼近。分别使用MOP、ZDT、DTLZ的部分基准套件测试算法性能。此外,通过多种基准算法对XGBoost模型进行优化对比。实验结果表明,AMOCOA在XGBoost模型参数优化问题上性能最佳,且在不同天气条件下所提模型整体预测误差最小,可有效提高预测精度。展开更多
To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy ...To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.展开更多
基金supported by National High Technology Research and Development Program of China(863 Program)(No.2007AA01Z416)National Natural Science Foundation of China(No.60773056)+1 种基金Beijing New Star Project on Science and Technology(No.2007B071)Natural Science Foundation of Liaoning Province of China(No.20052184)
文摘How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance.To address this problem,this paper introduces a novel image retrieval mechanism based on the family filtration in object region.First,we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object,retrieved from the corpus based on 100 images,as a result of the first rank.To further improve retrieval performance,we add an efficient spatial consistency stage,which is named family-based spatial consistency filtration,to re-rank the results returned by the first rank.We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of"TREC Video Retrieval Evaluation 2005(TRECVID2005)".The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality.The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.
文摘为提高光伏功率预测的准确性和稳定性,提出了一种新颖的基于自适应区域搜索的改进多目标浣熊优化算法(multi-objective Coati optimization algorithm with adaptive region exploration,AMOCOA),用于提升短期光伏功率预测模型性能。提出种群自适应划分策略提升算法搜索能力,引入多项式变异算子,扩大搜索区域,通过围捕路径优化策略确保种群向最优解逼近。分别使用MOP、ZDT、DTLZ的部分基准套件测试算法性能。此外,通过多种基准算法对XGBoost模型进行优化对比。实验结果表明,AMOCOA在XGBoost模型参数优化问题上性能最佳,且在不同天气条件下所提模型整体预测误差最小,可有效提高预测精度。
文摘To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.
