A new algorithm for bottom-up saliency estimation is proposed.Based on the sparse coding model,a power spectral filter is proposed to eliminate the second-order residual correlation,which suppresses the global repeate...A new algorithm for bottom-up saliency estimation is proposed.Based on the sparse coding model,a power spectral filter is proposed to eliminate the second-order residual correlation,which suppresses the global repeated items effectively.In addition,aiming at modeling the mechanism of the human retina prior response to high-contrast stimuli,the effect of color context is considered.Experiments on the three publicly available databases and some psychophysical images show that the proposed model is comparable with the state-of-the-art saliency models,which not only highlights the salient objects in a complex environment but also pops up them uniformly.展开更多
During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper propo...During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.展开更多
In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manua...In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane.The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition.One of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread fields.Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests.This is a tedious and time consuming process and does not suffice the accuracy levels.This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading.The digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the disease.The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning algorithm.To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages.Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection.The training vector thus developed is capable of presenting the relationship between the feature values and the target class.In this article,a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach i展开更多
基金Supported by the China Postdoctoral Science Foundation(2011M500917)the Jiangsu Innovation Program for Graduate Education(CXLX11_0180)
文摘A new algorithm for bottom-up saliency estimation is proposed.Based on the sparse coding model,a power spectral filter is proposed to eliminate the second-order residual correlation,which suppresses the global repeated items effectively.In addition,aiming at modeling the mechanism of the human retina prior response to high-contrast stimuli,the effect of color context is considered.Experiments on the three publicly available databases and some psychophysical images show that the proposed model is comparable with the state-of-the-art saliency models,which not only highlights the salient objects in a complex environment but also pops up them uniformly.
基金supported by the National Natural Science Foundation of China(Grant No.U1636115)the PAPD fund+1 种基金the CICAEET fundthe Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2017BDKFJJ017)
文摘During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.
基金This work was supported by Taif University Researchers Supporting Project(TURSP)under number(TURSP-2020/73)Taif University,Taif,Saudi Arabia。
文摘In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane.The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition.One of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread fields.Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests.This is a tedious and time consuming process and does not suffice the accuracy levels.This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading.The digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the disease.The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning algorithm.To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages.Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection.The training vector thus developed is capable of presenting the relationship between the feature values and the target class.In this article,a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach i
