基于边缘特征的遥感图像检索技术研究
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摘要
在基于内容的遥感图像检索中,如何有效地利用图像中的形状特征进行检索成为亟待解决的问题。边缘是空间信息的载体,从边缘特征中提取图像所包含的形状和纹理信息最为直接。本文重点研究了基于边缘特征遥感图像检索中的关键技术。针对基于全局特征的遥感图像检索中存在的问题,提出了相应的解决方案,并从理论上和实践上对其性能进行了分析和评价。主要研究内容和创新点如下:
1.介绍了广义Fisher信息测度,利用此工具系统地分析了形状特征的多尺度响应和纹理特征的多尺度响应,并将其作为边缘特征多尺度表达的理论基础。
2.改进了两组基于边缘图局部结构模式的检索方法,即多尺度边缘图基元矩和多尺度边缘图局部二值模式。实验证实,基于边缘图的检索方式具有特征计算花费小,特征维数低等特点。
3.引入了方向场的概念。针对边缘角度直方图表达信息的不足,采用多尺度技术进行了改进。通过Fourier算子的处理,在实现旋转不变性的同时进行了降维。改进后的直方图显著提高了对目标图像的检索性能。
4.针对空间域的角度直方图特征维数高,不利于相似度计算,改用变换域参数特征作为索引。重点研究了Contourlet变换理论,阐述了统计框架下基于内容的图像检索,提出了基于改进的子带能量特征的检索方法。与小波变换的比较发现,小波子带广义高斯模型参数特征适用于自然纹理图像的检索,而改进的Contourlet子带能量特征更适合于目标图像的检索。
5.研究了图像库的分块和分层组织结构。权衡覆盖率和存储量两项指标,采用五叉树对图像进行分块。在图像的分辨率转换中,提出了基于高斯函数的内插模型。单幅目标图像上所做的实验证实了此法不仅具有良好的目标和区域定位性能,而且能够适应分辨率变化的影响。
6.分析了边缘图特征与边缘方向特征的综合检索性能,指出通过合理分配权值可进一步提高对目标图像的检索精度。
In the application of content-based remote sensing image retrieval, how to retrieve images effectively through shape features has become an urgent problem. Edge is a carrier of spatial information. The methods to capture spatial information, contained in remote sensing images, from edge features for similarity matching are quite direct. This paper mainly researches some techniques in the field of edge-based remote sensing image retrieval. Aiming at the problems that are possibly encountered in the process of global-feature based image retrieval, some solutions are proposed, whose performances are theoretically analyzed and practically validated. The main work and innovations are as following:
1. The generalized Fisher information measure is presented. The multi-scale response of shape features and texture features is analyzed systematically through the above tool. It is used as the theoretical basis of multi-scale representation of edge features.
2. Two groups of retrieval methods, which are based on local structural pattern of edge maps, are improved. They are multi-scale edge map primitive moment and multi-scale edge map local binary pattern. The experiments confirm that the edge-map-based retrieval mode has such excellent characteristics as small time cost and low feature vector dimensions.
3. The concept of directional field is introduced. Aiming at the insufficiency of edge angular histogram to describe spatial information, it is improved by multi-scale analysis technique. Processed by Fourier operator, feature vectors become rotation-invariant, whose dimensions are reduced at the same time. The improved histogram enhances the retrieval quality for object images remarkably.
4. In the spatial domain, considering that the dimension of angle histogram is too large to be applied for similarity computation, some retrieval methods, which are based on parameters property of transform domain, are put forward. The theory of Contourlet transform is studied emphatically. A statistical analysis of the retrieval problem is expounded, and the modified energy characteristic is proposed. After comparing Contourlet transform with wavelet transform, it is found that the wavelet transform combining with generalized Gaussian model is fit for natural texture image retrieval, while the improved energy features of Contourlet’s subbands are more suitable for object image retrieval.
5. Partition and hierarchical structure schemes for image databases are proposed. Balancing the overlapping and storage index, images are partitioned to blocks by the Quin-tree method. In the application of image resolution transformation, an interpolation model, which is based on Gaussian function, is proposed. The experiments done on single remote sensing images confirm that the proposed schemes can locate objects or regions precisely and, at the same time, response flexibly to scale variations.
6. Combining the features based on edge map with edge directions, the performance of retrieval is analyzed. It is indicated that, through assigning reasonable weights, the retrieval performance for object images can be greatly enhanced.
1.介绍了广义Fisher信息测度,利用此工具系统地分析了形状特征的多尺度响应和纹理特征的多尺度响应,并将其作为边缘特征多尺度表达的理论基础。
2.改进了两组基于边缘图局部结构模式的检索方法,即多尺度边缘图基元矩和多尺度边缘图局部二值模式。实验证实,基于边缘图的检索方式具有特征计算花费小,特征维数低等特点。
3.引入了方向场的概念。针对边缘角度直方图表达信息的不足,采用多尺度技术进行了改进。通过Fourier算子的处理,在实现旋转不变性的同时进行了降维。改进后的直方图显著提高了对目标图像的检索性能。
4.针对空间域的角度直方图特征维数高,不利于相似度计算,改用变换域参数特征作为索引。重点研究了Contourlet变换理论,阐述了统计框架下基于内容的图像检索,提出了基于改进的子带能量特征的检索方法。与小波变换的比较发现,小波子带广义高斯模型参数特征适用于自然纹理图像的检索,而改进的Contourlet子带能量特征更适合于目标图像的检索。
5.研究了图像库的分块和分层组织结构。权衡覆盖率和存储量两项指标,采用五叉树对图像进行分块。在图像的分辨率转换中,提出了基于高斯函数的内插模型。单幅目标图像上所做的实验证实了此法不仅具有良好的目标和区域定位性能,而且能够适应分辨率变化的影响。
6.分析了边缘图特征与边缘方向特征的综合检索性能,指出通过合理分配权值可进一步提高对目标图像的检索精度。
In the application of content-based remote sensing image retrieval, how to retrieve images effectively through shape features has become an urgent problem. Edge is a carrier of spatial information. The methods to capture spatial information, contained in remote sensing images, from edge features for similarity matching are quite direct. This paper mainly researches some techniques in the field of edge-based remote sensing image retrieval. Aiming at the problems that are possibly encountered in the process of global-feature based image retrieval, some solutions are proposed, whose performances are theoretically analyzed and practically validated. The main work and innovations are as following:
1. The generalized Fisher information measure is presented. The multi-scale response of shape features and texture features is analyzed systematically through the above tool. It is used as the theoretical basis of multi-scale representation of edge features.
2. Two groups of retrieval methods, which are based on local structural pattern of edge maps, are improved. They are multi-scale edge map primitive moment and multi-scale edge map local binary pattern. The experiments confirm that the edge-map-based retrieval mode has such excellent characteristics as small time cost and low feature vector dimensions.
3. The concept of directional field is introduced. Aiming at the insufficiency of edge angular histogram to describe spatial information, it is improved by multi-scale analysis technique. Processed by Fourier operator, feature vectors become rotation-invariant, whose dimensions are reduced at the same time. The improved histogram enhances the retrieval quality for object images remarkably.
4. In the spatial domain, considering that the dimension of angle histogram is too large to be applied for similarity computation, some retrieval methods, which are based on parameters property of transform domain, are put forward. The theory of Contourlet transform is studied emphatically. A statistical analysis of the retrieval problem is expounded, and the modified energy characteristic is proposed. After comparing Contourlet transform with wavelet transform, it is found that the wavelet transform combining with generalized Gaussian model is fit for natural texture image retrieval, while the improved energy features of Contourlet’s subbands are more suitable for object image retrieval.
5. Partition and hierarchical structure schemes for image databases are proposed. Balancing the overlapping and storage index, images are partitioned to blocks by the Quin-tree method. In the application of image resolution transformation, an interpolation model, which is based on Gaussian function, is proposed. The experiments done on single remote sensing images confirm that the proposed schemes can locate objects or regions precisely and, at the same time, response flexibly to scale variations.
6. Combining the features based on edge map with edge directions, the performance of retrieval is analyzed. It is indicated that, through assigning reasonable weights, the retrieval performance for object images can be greatly enhanced.
引文
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[3]杨冉,卢朝阳.基于边缘特征的图像检索[J].微机发展,2005,15(1):24-26.
[4] Niblack W, Barber R, Flickner M. The QBIC Project: Querying Images by Content Using Color, Texture and Shape [A]. In: Proceeding of SPIE Conference on Visual Communication and Image Processing[C], 1994: 203-207.
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[7]程起敏.基于内容的遥感图像库检索关键技术研究[D].北京:中国科学院,2004.
[8] Bao P, Zhang X J. Image Retrieval Based on Multi-Scale Edge Model [J]. IEEE, 2002, 9 (2): 417-420.
[9] Jain A K, Vailaya A. Image Retrieval Using Color and Shape [J]. Pattern Recognition, 1996, 29(8): 1233-1244.
[10] Bober M. MPEG-7 Visual Shape Descriptor [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(6): 716-719.
[11] Jia X M, Wang G Y. Geometrical Partition of Edge Image: A New Approach for Image Structural Feature Description [A]. In: Proceeding of First International Conference on Machine Learning and Cybernetics[C], 2002: 1391-1395.
[12] Hirata K, Kato T. Query by Visual Example-Content Based Image Retrieval [J]. Proceeding of Advanced Database Technology, 1992: 56-71.
[13] Zhang D S, Lu G. Enhanced Generic Fourier Descriptors for Object-Based Image Retrieval [J]. Proceeding of IEEE International Conference on Acoustics, Speech, Signal Processing, 2002, 4: 3668-3671.
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[15] Zhou X S, Huang T S. Edge-Based Structural Features for Content-Based Image Retrieval [J]. Pattern Recognition Letters, 2001, 22: 457-468.
[16]韩军伟,郭雷.一个使用显著边缘进行图像检索的算法[J].模式识别与人工智能,2003,16(2):145-151.
[17] Chang H S, Kang K. A Compressed Domain Scheme for Classify Block Edge Patterns [J]. IEEE Transactions on Image Processing, 2005, 14(2): 145-151.
[18]邱开金,肖国强,江健民.基于游程编码的块边缘模式图像检索算法[J].计算机应用,2006,26(9):2074-2076.
[19]李弼程,彭天强,彭波等.智能图像处理技术[M].北京:电子工业出版社,2004:149-150.
[20]张祖勋,张剑清.数字摄影测量学[M].武汉:武汉测绘科技大学出版社,1996:118-128.
[21] Hadjidemetriou E, Grossberg M D, Nayar S K. Multiresolution Histograms and Their Use for Recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(7): 831-847.
[22] Sporring J, Weickert J. Information Measures in Scale-Spaces [J]. IEEE Transactions on Information Theory, 1999, 45(3): 1051-1058.
[23] Blackman N. The Convolution Inequality for Entropy Powers [J]. IEEE Transactions on Information Theory, 1965, 11: 267-271.
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[26] Teague M. Image Analysis via the General Theory of Moments [J]. Journal Optical Society of America, 1980, 70(8): 920-930.
[27] Chalechale A, Mertins A, Naghdy G. Edge Image Description Using Angular Radial Partitioning [J]. IEEE Proceeding of Vision, Image and Signal Processing, 2004, 151 (2): 93-101.
[28] Chalechale A, Naghdy G, Mertins A. Sketch-Based Image Matching Using Angular Partitioning [J]. IEEE Transactions on Systems, Man, and Cybernetics– Part A: System and Humans, 2005, 35 (1): 28-41.
[29]梁爽,孙正兴.面向草图检索的相关反馈方法[J].计算机辅助设计与图形学学报,2006,18 (11):1753-1757.
[30] Liang S, Sun Z X, Li B, et al. Effective Sketch Retrieval Based on Its Contents [A]. In: Proceeding of the Fourth International Conference on Machine Learning and Cybernetics[C], 2005: 18-21.
[31]王亮申,王文友,王红梅.基于灰度-基元共生矩阵的图像检索[J].辽阳石油化工高等专科学校学报,2002,18 (1):22-24.
[32]张云彬,张永生.基于图像纹理特征的目标快速检索[J].高技术通讯,2004,8:11-14.
[33] Haralick R, Shanmugan K, Dinstein I. Textural Features for Image Classification [J]. IEEE Transactions on Systems, Man, and Cybernetics 1973, 3: 610-621.
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