一种基于离散Tchebichef正交多项式和傅里叶梅林矩的局部多特征图像检索算法
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摘要
提出了一种基于离散Tchebichef正交多项式和傅里叶梅林矩的局部多特征图像检索算法。通过对图像进行正交变换和多分辨率重排序,在变换域中提取出纹理、颜色和形状特征,生成具有较强区分能力的图像特征。由于傅里叶梅林矩具有旋转不变性,因此在处理发生旋转变换和平移变换的图像时,检索效果较好。最后,对提出的算法用多个数据集进行了检索实验,并对实验结果进行了比较和分析。
With the rapid development of information science and technology,a large number of digital images are generated every day on the internet,leading to the demand for image retrieval tools in different fields.Because of the increasing amount of data,the traditional key word based image retrieval method is not able to meet the demand anymore.Therefore,it is becoming more and more urgent to develop a new image retrieval technology.In many image retrieval algorithms,the content-based image retrieval algorithm has been attracting the attention of the researchers in recent years.Different from the traditional way of retrieving by image name or other key words,the content-based image retrieval algorithm uses lowlevel image features,e.g.,color feature,texture feature,and shape feature,for image retrieval.Generally,the development of content-based image retrieval has experienced a process from single feature image retrieval to multi features image retrieval.Compared with global features,local features have advantages in dealing with occlusion,clutter and adaptation to partial appearance changes.As a result,the trend of CBIR research has shifted from global features to local features,which has been proven to be a practical way to deal with the semantic gap.In this paper,a local multi-feature image retrieval algorithm based on discrete Tchebichef orthogonal polynomial and Fourier Mellin moment is proposed.By orthogonally transforming and multiresolution reordering,the texture,color and shape features of images are extracted from the transform domain,and the image feature with strong distinguishing ability is generated.By considering the invariance of Fourier Mellin moment to rotation transformation,this proposed method can perform well in dealing with the image where the rotation transformation and the translation are performed.Finally,the multiple data sets are utilized to carry out the retrieval experiment and the experimental results are compared and analyzed.
With the rapid development of information science and technology,a large number of digital images are generated every day on the internet,leading to the demand for image retrieval tools in different fields.Because of the increasing amount of data,the traditional key word based image retrieval method is not able to meet the demand anymore.Therefore,it is becoming more and more urgent to develop a new image retrieval technology.In many image retrieval algorithms,the content-based image retrieval algorithm has been attracting the attention of the researchers in recent years.Different from the traditional way of retrieving by image name or other key words,the content-based image retrieval algorithm uses lowlevel image features,e.g.,color feature,texture feature,and shape feature,for image retrieval.Generally,the development of content-based image retrieval has experienced a process from single feature image retrieval to multi features image retrieval.Compared with global features,local features have advantages in dealing with occlusion,clutter and adaptation to partial appearance changes.As a result,the trend of CBIR research has shifted from global features to local features,which has been proven to be a practical way to deal with the semantic gap.In this paper,a local multi-feature image retrieval algorithm based on discrete Tchebichef orthogonal polynomial and Fourier Mellin moment is proposed.By orthogonally transforming and multiresolution reordering,the texture,color and shape features of images are extracted from the transform domain,and the image feature with strong distinguishing ability is generated.By considering the invariance of Fourier Mellin moment to rotation transformation,this proposed method can perform well in dealing with the image where the rotation transformation and the translation are performed.Finally,the multiple data sets are utilized to carry out the retrieval experiment and the experimental results are compared and analyzed.
引文
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[2]JAWORSKA T.Query Techniques for CBIR[M].Heidelberg:Springer International Publishing,2016.
[3]HIREMATH P S,PUJARI J.Content based image retrieval using color,texture and shape features[C]//International Conference on Advanced Computing and Communications.India:IEEE,2007:780-784.
[4]MALINGA B,RAICU D,FURST J.Local vs.global histogram-based color image clustering[R].USA:University of Depaul,2006.
[5]GUO X J,ZHU Y L,WANG Y F,et al.Research and implement the algorithm of image retrieval based on the multifeatures[C]//International Conference on Challenges in Environmental Science and Computer Engineering.USA:IEEE,2010:224-227.
[6]HUANG R B,DU M H.Object region based color image retrieval integrating multi-features[C]//International Conference on Wavelet Analysis and Pattern Recognition.USA:IEEE,2007:1729-1734.
[7]MUKUNDAN R.Transform coding using discrete tchebichef polynomials[C]//Proceedings of IASTED International Conference on Visualization Imaging and Image Processing.Spain:Actc Press,2006:270-275.
[8]ZHEN H,SHI M,YI Q.A block-based DCT and multiresolution reordering scheme for high resolution image coding[C]//International Conference on Consumer Electronics,Communications and Networks.USA:IEEE,2012:13-16.
[9]JAN F,TOMAS S,BARBARA Z.模式识别中的矩和矩不变量[M].北京:中国科学技术大学出版社,2014:179-182.
[10]NISTER D,STEWENIUS H.Scalable recognition with a vocabulary tree[C]//2006IEEE Computer Society Conference on Computer Vision and Pattern Recognition.USA:IEEE,2006:2161-2168.