The current deep convolution features based on retrievalmethods cannot fully use the characteristics of the salient image regions.Also,they cannot effectively suppress the background noises,so it is a challenging task...The current deep convolution features based on retrievalmethods cannot fully use the characteristics of the salient image regions.Also,they cannot effectively suppress the background noises,so it is a challenging task to retrieve objects in cluttered scenarios.To solve the problem,we propose a new image retrieval method that employs a novel feature aggregation approach with an attention mechanism and utilizes a combination of local and global features.The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism.After that,the feature aggregation mechanism aggregates the keypoints to a compact vector representation according to the scores evaluated by the attention mechanism.The core of the aggregation mechanism is to allow features with high scores to participate in residual operations of all cluster centers.Finally,we get the improved image representation by fusing aggregated feature descriptor and global feature of the input image.To effectively evaluate the proposedmethod,we have carried out a series of experiments on large-scale image datasets and compared them with other state-of-the-art methods.Experiments show that this method greatly improves the precision of image retrieval and computational efficiency.展开更多
The Hessian matrix has a wide range of applications in image processing,such as edge detection,feature point detection,etc.This paper proposes an image enhancement algorithm based on the Hessian matrix.First,the Hessi...The Hessian matrix has a wide range of applications in image processing,such as edge detection,feature point detection,etc.This paper proposes an image enhancement algorithm based on the Hessian matrix.First,the Hessian matrix is obtained by convolving the derivative of the Gaussian function.Then use the Hessian matrix to enhance the linear structure in the image.Experimental results show that the method proposed in this paper has strong robustness and accuracy.展开更多
基金This research is jointly supported by the National Natural Science Foundation of China(62072414,U1504608,61975187)the Foundation and Cutting-Edge Technologies Research Program of Henan Province(212102210540,192102210294,212102210280).
文摘The current deep convolution features based on retrievalmethods cannot fully use the characteristics of the salient image regions.Also,they cannot effectively suppress the background noises,so it is a challenging task to retrieve objects in cluttered scenarios.To solve the problem,we propose a new image retrieval method that employs a novel feature aggregation approach with an attention mechanism and utilizes a combination of local and global features.The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism.After that,the feature aggregation mechanism aggregates the keypoints to a compact vector representation according to the scores evaluated by the attention mechanism.The core of the aggregation mechanism is to allow features with high scores to participate in residual operations of all cluster centers.Finally,we get the improved image representation by fusing aggregated feature descriptor and global feature of the input image.To effectively evaluate the proposedmethod,we have carried out a series of experiments on large-scale image datasets and compared them with other state-of-the-art methods.Experiments show that this method greatly improves the precision of image retrieval and computational efficiency.
基金supported by the key scientific research projects of the Hunan Provincial Department of Education (No.19A099,20A102)the Educational Reform Project of the Hunan Provincial Department of Education (No.HNJG-2021-1121)+2 种基金the Hunan First Normal University Teaching Reform Project (No.XYS21J09)Shaoyang City Science and Technology Bureau Science and Technology Research Project (No.2020GX31)Shaoyang University Cooperation Project (No.2019HX115).
文摘The Hessian matrix has a wide range of applications in image processing,such as edge detection,feature point detection,etc.This paper proposes an image enhancement algorithm based on the Hessian matrix.First,the Hessian matrix is obtained by convolving the derivative of the Gaussian function.Then use the Hessian matrix to enhance the linear structure in the image.Experimental results show that the method proposed in this paper has strong robustness and accuracy.