一种基于区域的图像检索方法
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摘要
随着因特网和计算机视觉技术的发展,数字图像的产生、存储、分析和传输访问的数量呈指数级增长。全球每天都在不断地产生数以兆字节计数字图像数据,然而这些数据散乱地分布在全世界各个角落,人们不能进行有效地访问和利用。这就要求有一种能够快速而且准确地查找、访问图像的技术,也就是图像检索技术。
基于内容图像检索技术,从一定程度上解决了传统的基于文本图像检索技术的局限性,已经成为当前图像检索技术的热点研究领域。基于区域图像检索技术是基于内容图像检索技术的一个重要研究方向,它从对象层次上理解和表示图像,在一定程度上缩小了图像底层特征和高层语义间的鸿沟,更符合人类视觉对图像的认知。模糊数学是研究和处理不明确现象的一种数学理论和方法。人们经常接受模糊语言与模糊信息,并能做出正确的识别和判断,因此在图像检索中结合模糊数学理论成为基于内容图像检索新的趋势。
本文的主要工作有:
(1)设计并实现了基于内容图像检索原型系统,该系统是进行优秀算法测试和检索效果评估的科研平台。这个系统分为图像索引和图像检索两大部分:图像索引子系统采用离线方式提取图像库中所有候选图像特征,保存在本地;图像检索子系统在线执行,提取示例图像特征并进行相似性匹配,返回检索结果。
(2)深入分析了一种基于区域模糊集图像检索算法,在该算法的基础上,结合“面积优先”和“背景优先”,提出了颜色、纹理和形状特征间新的权重分配方案,并对形状特征实行特殊的匹配方案。基于区域模糊集图像检索算法采用动态k-均值聚类分析算法将图像分割成一个个区域,基于模糊集对每个区域提取模糊特征,定义了新的特征匹配方法对示例图像与候选图像进行相似性度量。实验结果表明,改进后的算法更符合人们对图像视觉特征的理解,具有良好的检索效率和检索结果。
With the development of the technology of Internet and computer vision, the volume of digital image archives is growing as an unbelievable speed. The very large repository of digital information raises challenging problems in retrieval and various other information manipulation tasks. Content-Based Image Retrieval (CBIR) is aimed at efficient retrieval of relevant images from large image database based on automatically derived imagery features. These features are typically extracted from shape, texture, or color properties of query image and image in the database. The relevance between a query image and any target image is ranked according to a similarity measure computed from the features.
A region-based image retrieval system segments images into regions or objects and retrieves images based on the similarity between regions. This is consistent to human recognition. In addition, applying fuzzy processing techniques to CBIR has been extensively investigated in the literature. To improve the robustness of a region-based image retrieval system against segmentation related uncertainties,“A Region-based Fuzzy Feature Approach to Content-based Image Retrieval”is selected as the theme of this paper.
In the paper, we develop a prototype system started with the accuracy and real time of the content-based image retrieval. We introduce a region-based image retrieval algorithm based on fuzzy logic theory. Every core technology applied in the algorithm is analyzed in detail, including image segmentation, fuzzy feature extracted and fuzzy feature match scheme. We propose a new weight scheme combined area percentage scheme and border favored scheme to provide an overall image-to-image and intuitive similarity quantification. In addition, we do some special operations to shape feature match since we cannot have excellent shape representation.
基于内容图像检索技术,从一定程度上解决了传统的基于文本图像检索技术的局限性,已经成为当前图像检索技术的热点研究领域。基于区域图像检索技术是基于内容图像检索技术的一个重要研究方向,它从对象层次上理解和表示图像,在一定程度上缩小了图像底层特征和高层语义间的鸿沟,更符合人类视觉对图像的认知。模糊数学是研究和处理不明确现象的一种数学理论和方法。人们经常接受模糊语言与模糊信息,并能做出正确的识别和判断,因此在图像检索中结合模糊数学理论成为基于内容图像检索新的趋势。
本文的主要工作有:
(1)设计并实现了基于内容图像检索原型系统,该系统是进行优秀算法测试和检索效果评估的科研平台。这个系统分为图像索引和图像检索两大部分:图像索引子系统采用离线方式提取图像库中所有候选图像特征,保存在本地;图像检索子系统在线执行,提取示例图像特征并进行相似性匹配,返回检索结果。
(2)深入分析了一种基于区域模糊集图像检索算法,在该算法的基础上,结合“面积优先”和“背景优先”,提出了颜色、纹理和形状特征间新的权重分配方案,并对形状特征实行特殊的匹配方案。基于区域模糊集图像检索算法采用动态k-均值聚类分析算法将图像分割成一个个区域,基于模糊集对每个区域提取模糊特征,定义了新的特征匹配方法对示例图像与候选图像进行相似性度量。实验结果表明,改进后的算法更符合人们对图像视觉特征的理解,具有良好的检索效率和检索结果。
With the development of the technology of Internet and computer vision, the volume of digital image archives is growing as an unbelievable speed. The very large repository of digital information raises challenging problems in retrieval and various other information manipulation tasks. Content-Based Image Retrieval (CBIR) is aimed at efficient retrieval of relevant images from large image database based on automatically derived imagery features. These features are typically extracted from shape, texture, or color properties of query image and image in the database. The relevance between a query image and any target image is ranked according to a similarity measure computed from the features.
A region-based image retrieval system segments images into regions or objects and retrieves images based on the similarity between regions. This is consistent to human recognition. In addition, applying fuzzy processing techniques to CBIR has been extensively investigated in the literature. To improve the robustness of a region-based image retrieval system against segmentation related uncertainties,“A Region-based Fuzzy Feature Approach to Content-based Image Retrieval”is selected as the theme of this paper.
In the paper, we develop a prototype system started with the accuracy and real time of the content-based image retrieval. We introduce a region-based image retrieval algorithm based on fuzzy logic theory. Every core technology applied in the algorithm is analyzed in detail, including image segmentation, fuzzy feature extracted and fuzzy feature match scheme. We propose a new weight scheme combined area percentage scheme and border favored scheme to provide an overall image-to-image and intuitive similarity quantification. In addition, we do some special operations to shape feature match since we cannot have excellent shape representation.
引文
[1]马修军.多媒体数据库与内容检索.北京:北京大学出版社,2008,55-91
[2]章毓敏.基于内容的视觉信息检索.北京:科学出版社,2003,2-20
[3] T. Gevers, A. W. M Smeulders. PicToSeek: Combining Color and Shape Invariant Features for Image Retrieval, IEEE Trans. Image Processing, 2000, Vol.9, no.1: 102-119
[4] J.Z. Wang, G. Wiederhold, O. Firchein, et al. Content-Based Image Indexing and Searching Using Daubechies’Wavelets. Digital Libraries, 1998, Vol.1, no.4: 311-328
[5] C. Schmid, R. Mohr. Local Grayvalue Invariants for Image Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 1997, Vol.19, no.5: 311-328
[6] I. J. Cox, M. L. Miller, T. P. Minka, et al. The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments. IEEE Trans. Image Process, 2000, Vol.9, no.1: 20-37
[7] Yan Ke, Rahul Sukthankar. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, Vol.2: 506-513
[8] Yixin Chen, James Z. Wang. A Region-Based Fuzzy Feature Matching Approach to Content Based Image Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, Vol.24, no.9: 1252-1267
[9]肖明.基于内容的多媒体信息索引与检索概论.北京:人民邮电出版社,2009,134-136
[10]李清勇,胡宏,施智平.基于纹理语义特征的图像检索研究.计算机学报,2006,Vol.29, no.1: 116-123
[11] Hwei-Jen Lin, Yang-Ta Kao, SHwu-Huey Yen. A Study of Shape-based Image Retrieval. IEEE Conference on Distributed Computing System Workshops, 2004, icdcsw, vol. 1: 118-123
[12] Iivari Kunttu, Leena Lepisto, Juhani Rauhamaa. Multiscale Fourier Descriptor for Shape-Based Image Retrieval. IEEE International Conference on Pattern Recognition, 2004, icpr, vol.2: 765-768
[13]汪彦龙,刘金华,王丽萍.基于对象空间关系的图像检索方法研究.计算机技术与发展,2006,Vol.16,no.1: 66-72
[14]周明全,耿国华,韦娜.基于内容图像检索技术.北京:清华大学出版社,2007,165-174
[15] K. Banard, P. Duygulu, N. Freitas. Matching words and Pictures,Journal of Machine Learning Research, 2003, Vol.3: 1107-1135
[16]温超,耿国华.基于内容图像检索中的“语义鸿沟”问题.西北大学学报:自然科学版. 2005: 536-540
[17] Ritendra Datta, Dhiraj Joshi, Jia Li, et al. Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Comput Surv. 2008, vol. 40, no. 2: 1-60
[18] H. Muller, A. Geissbuhler, S. Marchand-Maillet, et al. Benchmarking image retrieval applications. Visual Information System, 2004,vol.21: 55-73
[19] Ying Liu, Dengsheng Zhang, Guojun Lu, et al. A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40, 2007: 262-282
[20]谭晓阳,孙正兴,张福彦.交互式图像检索中的相关反馈技术研究进展.南京大学学报:自然科学版,2004,Vol.40,no.5:639-648
[21]王珏,石纯一.机器学习研究.广西师范大学学报:自然科学版,2003,Vol.21,no.2: 1-15
[22] A. W. M Smeulders, M. Worring, et al. Content-based image retrieval at the end of the early years. IEEE Trans. on Pattern and Machine Intelligence, 2000,Vol.22: 1349-1380
[23] B. S. Manjunath, Philippe Salembier, Thomas Sikora. Introduction to MPEG-7: multimedia content description interface. 2002: 214-219
[24] C. Faloutsos, R. Barber, M. Flickner, et al. Efficient and effective Querying by Image Content. Intelligent Information System, 1994, Vol.3, nos.3-4: 231-262
[25] A.Pentland, R.W. Picard, S. Sclaroff. Photobook: Content-Based Manipulation for Image Database. Computer Vision, 1996, Vol.18: 233-254
[26] S. Mehrotra, Y. Rui, M. Ortega-Binderberger, et al. Supporting Content-Based Queries over Images in MARS. IEEE Int’l Conf. Multimedia Computing and System, 1997: 632-633
[27]张春田,苏育挺,张静.数字图像压缩编码.北京:清华大学出版社,2006,214-250
[28] James Ze Wang, Gio Wiederhold, Oscar Firschein, et al. Conent-based image indexing and searching using Daubechies’s wavelets. Int J Digit Libr, 1997,Vol. 1: 311-328
[29]杨纶标,高英仪.模糊数学原理及应用.广州:华南理工大学出版社,2005,1-21
[30] C. Vertan and N. Boujemaa. Embedding Fuzzy Logic in Content Based Image Retrieval. 19th Int’l Meeting North Am. 2000: 85-89
[31] Rafael C. Gonzalez, Richard E. Woods.数字图像处理.电子工业出版社,2007,117-170
[32]刘友明,刘希顺,刘安之.一种基于LUV均匀颜色空间的彩色分割方法.微型电脑应用,2000,Vol.16, no.12: 1007-757X
[33]于殿泓.图像检测与处理技术.西安:西安电子科技大学出版社,2006,145-169
[34] Amir Ahmad, Lipika Dey. A k-mean clustering algorithm for mixed numeric and categorical data. Data and Knowledge Engineering, 2007, Vol.63: 503-527
[35] A. Gersho. Asympotically Optimum Block Quantization. IEEE Trans. Information Theory, 1979, Vol.25, no.4: 273-383
[36] J. Li, J. Z. Wang, G. Wiederhold. IRM: Integrated Region Matching for Image Retrieval. Eighth ACM Int’l Conf. Multimedia, 2000: 147-156
[37] F.Hoppner, F.Klawonn, R. Kruse, et al. Fuzzy Cluster Analysis: Method for Classification. Data Analysis and Image Recognition, John Viley & Sons, 1999: 431-450
[38] J. Li, J. Z. Wang and G. Wiederhold. Classification of Textured and Non-Textured Image Using Region Segmentation. Seventh Int’l Conf. Image Processing, 2000: 754-757
[39]徐全智,吕恕.概率论与数理统计.北京:高等教育出版社,2004,63-72
[40]刘瑞帧,于仕琪. OpenCV教程基础篇.北京:北京航天航空大学出版社,2007,2-16
[2]章毓敏.基于内容的视觉信息检索.北京:科学出版社,2003,2-20
[3] T. Gevers, A. W. M Smeulders. PicToSeek: Combining Color and Shape Invariant Features for Image Retrieval, IEEE Trans. Image Processing, 2000, Vol.9, no.1: 102-119
[4] J.Z. Wang, G. Wiederhold, O. Firchein, et al. Content-Based Image Indexing and Searching Using Daubechies’Wavelets. Digital Libraries, 1998, Vol.1, no.4: 311-328
[5] C. Schmid, R. Mohr. Local Grayvalue Invariants for Image Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 1997, Vol.19, no.5: 311-328
[6] I. J. Cox, M. L. Miller, T. P. Minka, et al. The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments. IEEE Trans. Image Process, 2000, Vol.9, no.1: 20-37
[7] Yan Ke, Rahul Sukthankar. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, Vol.2: 506-513
[8] Yixin Chen, James Z. Wang. A Region-Based Fuzzy Feature Matching Approach to Content Based Image Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, Vol.24, no.9: 1252-1267
[9]肖明.基于内容的多媒体信息索引与检索概论.北京:人民邮电出版社,2009,134-136
[10]李清勇,胡宏,施智平.基于纹理语义特征的图像检索研究.计算机学报,2006,Vol.29, no.1: 116-123
[11] Hwei-Jen Lin, Yang-Ta Kao, SHwu-Huey Yen. A Study of Shape-based Image Retrieval. IEEE Conference on Distributed Computing System Workshops, 2004, icdcsw, vol. 1: 118-123
[12] Iivari Kunttu, Leena Lepisto, Juhani Rauhamaa. Multiscale Fourier Descriptor for Shape-Based Image Retrieval. IEEE International Conference on Pattern Recognition, 2004, icpr, vol.2: 765-768
[13]汪彦龙,刘金华,王丽萍.基于对象空间关系的图像检索方法研究.计算机技术与发展,2006,Vol.16,no.1: 66-72
[14]周明全,耿国华,韦娜.基于内容图像检索技术.北京:清华大学出版社,2007,165-174
[15] K. Banard, P. Duygulu, N. Freitas. Matching words and Pictures,Journal of Machine Learning Research, 2003, Vol.3: 1107-1135
[16]温超,耿国华.基于内容图像检索中的“语义鸿沟”问题.西北大学学报:自然科学版. 2005: 536-540
[17] Ritendra Datta, Dhiraj Joshi, Jia Li, et al. Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Comput Surv. 2008, vol. 40, no. 2: 1-60
[18] H. Muller, A. Geissbuhler, S. Marchand-Maillet, et al. Benchmarking image retrieval applications. Visual Information System, 2004,vol.21: 55-73
[19] Ying Liu, Dengsheng Zhang, Guojun Lu, et al. A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40, 2007: 262-282
[20]谭晓阳,孙正兴,张福彦.交互式图像检索中的相关反馈技术研究进展.南京大学学报:自然科学版,2004,Vol.40,no.5:639-648
[21]王珏,石纯一.机器学习研究.广西师范大学学报:自然科学版,2003,Vol.21,no.2: 1-15
[22] A. W. M Smeulders, M. Worring, et al. Content-based image retrieval at the end of the early years. IEEE Trans. on Pattern and Machine Intelligence, 2000,Vol.22: 1349-1380
[23] B. S. Manjunath, Philippe Salembier, Thomas Sikora. Introduction to MPEG-7: multimedia content description interface. 2002: 214-219
[24] C. Faloutsos, R. Barber, M. Flickner, et al. Efficient and effective Querying by Image Content. Intelligent Information System, 1994, Vol.3, nos.3-4: 231-262
[25] A.Pentland, R.W. Picard, S. Sclaroff. Photobook: Content-Based Manipulation for Image Database. Computer Vision, 1996, Vol.18: 233-254
[26] S. Mehrotra, Y. Rui, M. Ortega-Binderberger, et al. Supporting Content-Based Queries over Images in MARS. IEEE Int’l Conf. Multimedia Computing and System, 1997: 632-633
[27]张春田,苏育挺,张静.数字图像压缩编码.北京:清华大学出版社,2006,214-250
[28] James Ze Wang, Gio Wiederhold, Oscar Firschein, et al. Conent-based image indexing and searching using Daubechies’s wavelets. Int J Digit Libr, 1997,Vol. 1: 311-328
[29]杨纶标,高英仪.模糊数学原理及应用.广州:华南理工大学出版社,2005,1-21
[30] C. Vertan and N. Boujemaa. Embedding Fuzzy Logic in Content Based Image Retrieval. 19th Int’l Meeting North Am. 2000: 85-89
[31] Rafael C. Gonzalez, Richard E. Woods.数字图像处理.电子工业出版社,2007,117-170
[32]刘友明,刘希顺,刘安之.一种基于LUV均匀颜色空间的彩色分割方法.微型电脑应用,2000,Vol.16, no.12: 1007-757X
[33]于殿泓.图像检测与处理技术.西安:西安电子科技大学出版社,2006,145-169
[34] Amir Ahmad, Lipika Dey. A k-mean clustering algorithm for mixed numeric and categorical data. Data and Knowledge Engineering, 2007, Vol.63: 503-527
[35] A. Gersho. Asympotically Optimum Block Quantization. IEEE Trans. Information Theory, 1979, Vol.25, no.4: 273-383
[36] J. Li, J. Z. Wang, G. Wiederhold. IRM: Integrated Region Matching for Image Retrieval. Eighth ACM Int’l Conf. Multimedia, 2000: 147-156
[37] F.Hoppner, F.Klawonn, R. Kruse, et al. Fuzzy Cluster Analysis: Method for Classification. Data Analysis and Image Recognition, John Viley & Sons, 1999: 431-450
[38] J. Li, J. Z. Wang and G. Wiederhold. Classification of Textured and Non-Textured Image Using Region Segmentation. Seventh Int’l Conf. Image Processing, 2000: 754-757
[39]徐全智,吕恕.概率论与数理统计.北京:高等教育出版社,2004,63-72
[40]刘瑞帧,于仕琪. OpenCV教程基础篇.北京:北京航天航空大学出版社,2007,2-16
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