中山大学学报(自然科学版)
中山大學學報(自然科學版)
중산대학학보(자연과학판)
ACTA SCIENTIARUM NATURALIUM UNIVERSITATIS SUNYATSENI
2014年
1期
57-62,66
,共7页
周燕%曾凡智%卢炎生%周月霞
週燕%曾凡智%盧炎生%週月霞
주연%증범지%로염생%주월하
图像检索%压缩感知%测量矩阵%特征提取%特征匹配
圖像檢索%壓縮感知%測量矩陣%特徵提取%特徵匹配
도상검색%압축감지%측량구진%특정제취%특정필배
image retrieval%compressed sensing%measurement matrix%feature extraction%feature matc-hing
针对大尺寸图像的特征提取算法复杂度高、特征信息容易缺失的问题,利用压缩感知理论中关于少量测量值可以精确重构原始信号的特性,提出了一种基于压缩感知的图像检索方法。首先对图像进行小波变换、分块预处理;然后构造分块多项式确定性测量矩阵,并对分块图像进行压缩感知快速测量,得到少量的压缩测量值代表图像的特征;最后采用加权距离方法计算图像测量值特征的相似度,实现图像的精确检索。仿真结果表明,该方法在图像检索速度和查准率、查全率等指标上具有更高的性能,能应用于大量复杂图像的检索。
針對大呎吋圖像的特徵提取算法複雜度高、特徵信息容易缺失的問題,利用壓縮感知理論中關于少量測量值可以精確重構原始信號的特性,提齣瞭一種基于壓縮感知的圖像檢索方法。首先對圖像進行小波變換、分塊預處理;然後構造分塊多項式確定性測量矩陣,併對分塊圖像進行壓縮感知快速測量,得到少量的壓縮測量值代錶圖像的特徵;最後採用加權距離方法計算圖像測量值特徵的相似度,實現圖像的精確檢索。倣真結果錶明,該方法在圖像檢索速度和查準率、查全率等指標上具有更高的性能,能應用于大量複雜圖像的檢索。
침대대척촌도상적특정제취산법복잡도고、특정신식용역결실적문제,이용압축감지이론중관우소량측량치가이정학중구원시신호적특성,제출료일충기우압축감지적도상검색방법。수선대도상진행소파변환、분괴예처리;연후구조분괴다항식학정성측량구진,병대분괴도상진행압축감지쾌속측량,득도소량적압축측량치대표도상적특정;최후채용가권거리방법계산도상측량치특정적상사도,실현도상적정학검색。방진결과표명,해방법재도상검색속도화사준솔、사전솔등지표상구유경고적성능,능응용우대량복잡도상적검색。
For solving the problems of the complexity about feature extraction on large-size image and the loss of feature information,the characteristics of compressive sensing (CS)theory that a small amount of measurements can accurately reconstruct the original signal is used,and a new image retrieval method based on compressive sensing is proposed.First,the wavelet transformation is performed,and the image is divided into blocks.Then,blocked polynomial deterministic measurement matrix and conduct fast CS measurement on blocked image constructed,and very few compressive measurements which represent the image features can be obtained.Finally,we calculate the similarity between the measurement features by weight distance is calculated,so to implement accurate image retrieval.At the same time,it is proved theoretically that the blocked polynomial deterministic measurement matrix is satisfied to the restricted i-sometric property (RIP).Experimental results show that this method has higher performance on image precision and image recall,and can be applied to massive complex image retrieval.