基于内容的图像检索与分类若干技术的研究
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摘要
随着科学技术的进步和多媒体技术的发展,以及互联网络的迅速普及,图像数据呈现出爆炸性的增长趋势。如何迅速而准确地从浩瀚的图像数据库中检索到所需的图像成了多年来多媒体领域的研究热点。为了能够快速而准确地找到想要的图像,利用图像内容,如颜色、形状、纹理等特征来检索图像的技术应运而生。这项技术被称为基于内容的图像检索(Content-based Image Retrieval)技术。由于其具有重要的研究意义和巨大的实用价值而得到研究人员们的重视。虽然基于内容的图像检索技术己经有了大量的研究和实际的应用,但是其中许多技术还不成熟,还需要进一步的研究和改进。本文针对其中的一些关键技术和理论方法,作了如下几个方面的工作:
1)研究介绍了三种图像检索类型,它们分别是:基于内容的图像检索、基于语义的图像检索以及基于反馈机制的图像检索,着重研究了基于内容图像检索的关键技术。
2)提出了提取目标区域主色调,并将主色调与颜色量化相结合的图像检索方法,对于目标区域的选择,我们要求用户在给出检索示例图像的同时,用矩形框指出感兴趣的区域。实验证明,该算法能够得到比较令人满意的检索结果。
3)简要概述了图像分类技术,提出了基于图像分割的图像分类算法。该算法基于HSV空间,首先用基于阈值方法对图像进行分割,得到目标区域,依据规则选取前景区域,提取前景区域的颜色特征向量,输入支持向量机,完成图像分类。实验表明,算法的设计和支持向量机参数的选择是比较合理的,能够得到比较令人满意的分类结果。还介绍了深度网、文本分类以及主题爬行的相关内容,对深度网主题爬行方法提出了改进。
With the development of technology and the rapid expansion of the Internet, there has been an increasing number of image data, followed by the emergence of a growing number of large image database, the network also carries vast amounts of image data resources.
Since the 70’s of last century, there have been text-based image retrieval. Text-based image retrieval developed from the text retrieval technology using text label of image.It’s key technology is text retrieval. Until the 90's of last century, people began to study content-based image retrieval. Content-based image retrieval extract features such as color, texture, shape, spatial location of image automatically. Content-based image retrieval has obvious advantages: no longer labeling images manually, which means search results will not be affected by the subjective dimension; improve the efficiency of image retrieval.
There are three kinds of image retrieval methods: content-based, semantic-based and feedback-based retrieval, and content-based image retrieval is the most popular one of all. Among various physical characteristics of the image, Color feature is one of the most basic features, and is widely used by retrieval algorithms. The color feature will not change when position or rotation situation of image change. However, as the color image is usually colorful, the dimension of color feature vectors is very high which resulting in increased computation. The time consumption become very large When the image set grows large. There are usually two way to solute this question. One is to define several color region, similar color is classified into same region.And the dimension of vectors will decrease accordingly. Another way is to calculate the histogram, choose the most frequent colors (dominant color), as feature vector.
Image Classification is closely contacting with image retrieval. The key procedures of image classification include: image preprocessing(Image Enhancement, image denoising, and image restoration,etc.), feature vector extraction, build classification model and image classification. Three popular classifiers are: statistics-based, rule-based and artificial neural network based classifier. Support Vector Machine as a machine learning method developed from stastics method has great advantages in small sample, nonlinear or high-dimensional pattern recognition problems.
This paper introduces three kinds of retrieval methods, discusses popular feature of image and the similarity measurement methods, explains two algorithms in detail: one is Image Retrieval algorithm, elaborates image retrieval algorithm integrating dominant color and color quantization, the other is Image Classification altorithm based on Support Vector Machine.
The contribution of this paper is summarized as follows:
1) This paper presents image retrieval algorithm integrating dominant color and color quantization. First, user of retrieval system should select one image as demo image, and also specify the region of interest(ROI) with rectangle. Second, we calculate histogram of ROI and choose dominant colors, then quantize color of demo image. Third, calculate the weight of every color region according to dominant color information, and finally form feature vector of demo image.
2) This paper presents image classification algorithm. The algorithm integrates existing image segmentation method based on threshold and Support Vector Machine, presents rules for foreground image judging. Execution processs are: first, convert color image to gray image, segment demo image into several areas and find out the foreground image with judging rules, calculate vector of color feature of foreground image, at last train the Support Vector Machine. Conducts the research on deep Web resource search, designs a deep Web vertical search method.
1)研究介绍了三种图像检索类型,它们分别是:基于内容的图像检索、基于语义的图像检索以及基于反馈机制的图像检索,着重研究了基于内容图像检索的关键技术。
2)提出了提取目标区域主色调,并将主色调与颜色量化相结合的图像检索方法,对于目标区域的选择,我们要求用户在给出检索示例图像的同时,用矩形框指出感兴趣的区域。实验证明,该算法能够得到比较令人满意的检索结果。
3)简要概述了图像分类技术,提出了基于图像分割的图像分类算法。该算法基于HSV空间,首先用基于阈值方法对图像进行分割,得到目标区域,依据规则选取前景区域,提取前景区域的颜色特征向量,输入支持向量机,完成图像分类。实验表明,算法的设计和支持向量机参数的选择是比较合理的,能够得到比较令人满意的分类结果。还介绍了深度网、文本分类以及主题爬行的相关内容,对深度网主题爬行方法提出了改进。
With the development of technology and the rapid expansion of the Internet, there has been an increasing number of image data, followed by the emergence of a growing number of large image database, the network also carries vast amounts of image data resources.
Since the 70’s of last century, there have been text-based image retrieval. Text-based image retrieval developed from the text retrieval technology using text label of image.It’s key technology is text retrieval. Until the 90's of last century, people began to study content-based image retrieval. Content-based image retrieval extract features such as color, texture, shape, spatial location of image automatically. Content-based image retrieval has obvious advantages: no longer labeling images manually, which means search results will not be affected by the subjective dimension; improve the efficiency of image retrieval.
There are three kinds of image retrieval methods: content-based, semantic-based and feedback-based retrieval, and content-based image retrieval is the most popular one of all. Among various physical characteristics of the image, Color feature is one of the most basic features, and is widely used by retrieval algorithms. The color feature will not change when position or rotation situation of image change. However, as the color image is usually colorful, the dimension of color feature vectors is very high which resulting in increased computation. The time consumption become very large When the image set grows large. There are usually two way to solute this question. One is to define several color region, similar color is classified into same region.And the dimension of vectors will decrease accordingly. Another way is to calculate the histogram, choose the most frequent colors (dominant color), as feature vector.
Image Classification is closely contacting with image retrieval. The key procedures of image classification include: image preprocessing(Image Enhancement, image denoising, and image restoration,etc.), feature vector extraction, build classification model and image classification. Three popular classifiers are: statistics-based, rule-based and artificial neural network based classifier. Support Vector Machine as a machine learning method developed from stastics method has great advantages in small sample, nonlinear or high-dimensional pattern recognition problems.
This paper introduces three kinds of retrieval methods, discusses popular feature of image and the similarity measurement methods, explains two algorithms in detail: one is Image Retrieval algorithm, elaborates image retrieval algorithm integrating dominant color and color quantization, the other is Image Classification altorithm based on Support Vector Machine.
The contribution of this paper is summarized as follows:
1) This paper presents image retrieval algorithm integrating dominant color and color quantization. First, user of retrieval system should select one image as demo image, and also specify the region of interest(ROI) with rectangle. Second, we calculate histogram of ROI and choose dominant colors, then quantize color of demo image. Third, calculate the weight of every color region according to dominant color information, and finally form feature vector of demo image.
2) This paper presents image classification algorithm. The algorithm integrates existing image segmentation method based on threshold and Support Vector Machine, presents rules for foreground image judging. Execution processs are: first, convert color image to gray image, segment demo image into several areas and find out the foreground image with judging rules, calculate vector of color feature of foreground image, at last train the Support Vector Machine. Conducts the research on deep Web resource search, designs a deep Web vertical search method.
引文
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[3] A Yoshitaka ,T Ichikawa. A Survey on Content-based Retrieval for Multimedia Databases [J] . IEEE Transactions on Knowledge and Data Engineering ,1999 ,11 (1) :81 - 93.
[4] Yong Rui , T S Huang , S F Chang . Image Retrieval : Current techniques , promising directions , and open issues [J] . Journal of Visual Communication and Image Representation , 1999 , 10 : 39 - 62
[5] FLICKNER M, NIBLACK W, et al, Query by image and video content : the QBIC system [J] .IEEE Computer, 1995, 28(9), 23-32.
[6] Bach J Fuller C , GuPtaA , et al. The Virage Image Seach Engine : An Open Framework for Image Management[C] . In Proc. SPIE , Storage and Retrieval for Still Image and Video DatabaseIV , vol.2670 . SanJose , CA , USA , 1996:76 - 87
[7] Pentland A , Pieard R W , Sclarorr S . Photobook : Tools for content-based manipulation of image databases[J] . Storage and Retrieval for Image and Video DatabaseⅡ, 1996 : 34 - 47 .
[8] J Dowe. Content-based retrieval in multimedia imaging[C]. In: W Niblack, R C Jain eds. Proc of SPIE Storage and Retrieval for Image and Video Databases, Vol 1908. San Joes, CA, USA: SPIE Press, 1993:164-167
[9] Pentland A, Picard R W, Sclaroffs. Photobook: Content-based Manipulation of Image Databases[J]. International Journal of Computer Vision, 1996,18(3):233-254.
[10] John R SMITH, Shih-fu CHANG, VisualSEEK: a fully automated content-based image query system, ACM Multimedia96, November 20, 1996
[11]张振花.基于内容的图像检索的若干技术研究[D].长春:吉林大学,2009:9-10
[12]黄祥林,沈兰荪.基于内容的图像检索技术研究[J].电子学报,2002,Vol.30 No.7:1065-1071
[13]罗永兴,于明,陈雷.基于内容的图像检索系统的研究[J].计算机与数字工程,2004,Vol.32 No.6:92-101
[14]徐杰,施鹏飞.基于内容的图像检索技术[J].中国图形图像学报,2003 ,Vol.8(A) No.9 : 977-983
[15]庄越挺,潘云鹤.基于内容的图像检索综述[J].模式识别与人工智能,1999,Vol.12 No.2 : 170– 177
[16]章毓晋.图像处理和分析[M].北京:清华大学出版社,2000: 19-20
[17] M. Stricker . M. Orengo, Similarity of color images [J]. SPIE Storage and Retrieval for Image and Video Databases III . 1995 . 2 Vol. 2185 : 381– 392
[18] G. Pass . R. Zabih . Histogram refinement for content-based image retrieval[C] . IEEE Workshop on Applications of Computer Vision.1996: 96-102 .
[19] J. Mao . A. K. Jain . Texture classification and segmentation using multiresolution simultaneous autoregressive models[J] . Pattern Recognition. 1992. Vol. 25 No. 2 : 173-188 .
[20] John R. Smith . Shih-Fu Chang. Tools and techniques for color image retrieval. In Proc. of SPIE: Storage and Retrieval for Image and Video Database. 1995 . Vol.2670.
[21] John R. Smith . Shih-Fu Chang. Automated binary texture feature sets for image retrieval[C]. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Proc.1996.5.
[22] Robert M. Haralick . K. Shanmugam . Its’hak Dinstein. Texture features for image classification. IEEE Trans. On Sys, Man, and Cyb, SMC-3(6):610-621, 1973.
[23] H. Tamura, S. Mori, and T. Yamawaki, Texture features corresponding to visual perception. IEEE Trans. On Systems, Man, and Cybernetics. 1978.6. Vol. Smc-8 No.6.
[24]刘龙飞,陈云浩,李京.遥感影像纹理分析方法综述与展望[J].遥感技术与应用,2003,Vol.18 No.6: 441-447
[25]马继红,师军.基于内容的图像检索技术研究[J].郑州轻工业学院学报,2009,Vol.24 No.4: 50-55
[26] S.-K. Chang, Q. Y. Shi, and C. Y. Yan. Iconic indexing by 2-D strings. IEEE Trans. Pattern Anal. Machine Intell., 9(3), pp. 413-428, May 1987.
[27] V. N. Gudivada and V. V. Raghavan. Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Trans. on Information Systems, vol. 13, no. 2, pp. 115-144, April 1995.
[28]张菁,沈兰荪.图像搜索中人机交互技术的新进展[J].智能系统学报,2007,Vol.2 No.4:14-20
[29]章毓晋.图像分割[M].北京:科学技术出版社,2001:2-3
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