A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering ...A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.展开更多
Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features ma...Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.展开更多
String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that...String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.展开更多
Some depression cells with heights lower than their surrounding cells may often be found in Grid-based digital elevation models (DEM) dataset due to sampling errors.The depression-filling algorithm presented by Planch...Some depression cells with heights lower than their surrounding cells may often be found in Grid-based digital elevation models (DEM) dataset due to sampling errors.The depression-filling algorithm presented by Planchon and Darboux works very quickly compared to other published methods.Despite its simplicity and deli-cacy,this algorithm remains difficult to understand due to its three complex subroutines and its recursive execution.Another fast algorithm is presented in this article.The main idea of this new algorithm is as follows:first,the DEM dataset is viewed as an island and the outer space as an ocean;when the ocean level increases,the DEM cells on the island's boundary will be inundated;when a cell is inundated for the first time,its elevation is increased to the ocean level at that moment;after the ocean has inun-dated the entire DEM,all of the depressions are filled.The depression-removing processing is performed using a priority queue.Theoretically,this new algorithm is a fast algorithm despite the fact that it runs more slowly than Planchon and Darboux's method.Its time-complexity in both the worst case and in an average case is O(8nlog 2 (m)),which is close to O(n).The running speed of this algorithm depends mainly on the insertion operation of the priority queue.As shown by the tests,the depres-sion-filling effects of this algorithm are correct and valid,and the overall time consumption of this algorithm is less than twice the time consumed by Planchon & Darboux's method for handling a DEM smaller than 2500×2500 cells.More importantly,this new algorithm is simpler and easier to understand than Planchon and Darboux's method This advantage allows the correct program code to be written quickly.展开更多
文摘A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.
基金supported by the UGC, SERO, Hyderabad under FDP during XI plan periodthe UGC, New Delhi for financial assistance under major research project Grant No. F-34-105/2008
文摘Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.
文摘String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.
基金financially supported by the National Basic Research Program of China (Grant No.2006CB400502)the Promotion of 100 Young Talent Scientist Project of the Chinese Acad-emy of Sciences (8-057493)the Special Meteorology Project(GYHY(QX)2007-6-1)
文摘Some depression cells with heights lower than their surrounding cells may often be found in Grid-based digital elevation models (DEM) dataset due to sampling errors.The depression-filling algorithm presented by Planchon and Darboux works very quickly compared to other published methods.Despite its simplicity and deli-cacy,this algorithm remains difficult to understand due to its three complex subroutines and its recursive execution.Another fast algorithm is presented in this article.The main idea of this new algorithm is as follows:first,the DEM dataset is viewed as an island and the outer space as an ocean;when the ocean level increases,the DEM cells on the island's boundary will be inundated;when a cell is inundated for the first time,its elevation is increased to the ocean level at that moment;after the ocean has inun-dated the entire DEM,all of the depressions are filled.The depression-removing processing is performed using a priority queue.Theoretically,this new algorithm is a fast algorithm despite the fact that it runs more slowly than Planchon and Darboux's method.Its time-complexity in both the worst case and in an average case is O(8nlog 2 (m)),which is close to O(n).The running speed of this algorithm depends mainly on the insertion operation of the priority queue.As shown by the tests,the depres-sion-filling effects of this algorithm are correct and valid,and the overall time consumption of this algorithm is less than twice the time consumed by Planchon & Darboux's method for handling a DEM smaller than 2500×2500 cells.More importantly,this new algorithm is simpler and easier to understand than Planchon and Darboux's method This advantage allows the correct program code to be written quickly.
