针对传统局部二值模式(LBP)在同时解决噪声、旋转和尺度变化问题时的不足,提出一种尺度不变的主方向自适应阈值的局部二进制算法。通过高斯滤波构建原始图像的多尺度空间;为提高旋转不变性和抗噪声能力,提出具有主方向的自适应阈值的二...针对传统局部二值模式(LBP)在同时解决噪声、旋转和尺度变化问题时的不足,提出一种尺度不变的主方向自适应阈值的局部二进制算法。通过高斯滤波构建原始图像的多尺度空间;为提高旋转不变性和抗噪声能力,提出具有主方向的自适应阈值的二值模式(dominant direction self-adaptive threshold LBP,DSLBP);取不同尺度图像的DSLBP特征均值作为图像的特征。在两大代表性的纹理数据库和布料纹理库上进行图像检索,实验结果表明,该算法能较好地解决噪声、旋转、和多尺度问题,获得了较好的检索精度。展开更多
This paper investigates the problem of retrieving aerial scene images by using semantic sketches, since the state-of-the-art retrieval systems turn out to be invalid when there is no exemplar query aerial image availa...This paper investigates the problem of retrieving aerial scene images by using semantic sketches, since the state-of-the-art retrieval systems turn out to be invalid when there is no exemplar query aerial image available. However, due to the complex surface structures and huge variations of resolutions of aerial images, it is very challenging to retrieve aerial images with sketches and few studies have been devoted to this task. In this article, for the first time to our knowledge, we propose a framework to bridge the gap between sketches and aerial images. First, an aerial sketch-image database is collected, and the images and sketches it contains are augmented to various levels of details. We then train a multi-scale deep model by the new dataset. The fully-connected layers of the network in each scale are finally connected and used as cross-domain features, and the Euclidean distance is used to measure the cross-domain similarity between aerial images and sketches. Experiments on several commonly used aerial image datasets demonstrate the superiority of the proposed method compared with the traditional approaches.展开更多
文摘针对传统局部二值模式(LBP)在同时解决噪声、旋转和尺度变化问题时的不足,提出一种尺度不变的主方向自适应阈值的局部二进制算法。通过高斯滤波构建原始图像的多尺度空间;为提高旋转不变性和抗噪声能力,提出具有主方向的自适应阈值的二值模式(dominant direction self-adaptive threshold LBP,DSLBP);取不同尺度图像的DSLBP特征均值作为图像的特征。在两大代表性的纹理数据库和布料纹理库上进行图像检索,实验结果表明,该算法能较好地解决噪声、旋转、和多尺度问题,获得了较好的检索精度。
基金supported by the National Natural Science Foundation of China(No.60433020,No.60673099,No.60773095)the"Computing and Software Science and Technology Innovation Flat"of project 985+2 种基金the Key Laboratory for Symbol Computation and Knowl-edge Engineering of the National Education Ministry of China(No.02090)the Science and Technology Development Planning Project of Jilin Province(No.20080168)the Jilin University Graduate Innovation Foundation of"Project 985"(No.20080236)
文摘This paper investigates the problem of retrieving aerial scene images by using semantic sketches, since the state-of-the-art retrieval systems turn out to be invalid when there is no exemplar query aerial image available. However, due to the complex surface structures and huge variations of resolutions of aerial images, it is very challenging to retrieve aerial images with sketches and few studies have been devoted to this task. In this article, for the first time to our knowledge, we propose a framework to bridge the gap between sketches and aerial images. First, an aerial sketch-image database is collected, and the images and sketches it contains are augmented to various levels of details. We then train a multi-scale deep model by the new dataset. The fully-connected layers of the network in each scale are finally connected and used as cross-domain features, and the Euclidean distance is used to measure the cross-domain similarity between aerial images and sketches. Experiments on several commonly used aerial image datasets demonstrate the superiority of the proposed method compared with the traditional approaches.
