基于局部熵的圖像特征描述方法
發(fā)布時間：2019-06-26 21:48
【摘要】：使用傳統(tǒng)的特征描述方法SIFT在單一尺度上描述圖像特征會丟失一部分重要信息,影響圖像的正確匹配結(jié)果。為了解決這一問題,本文在多尺度模糊空間內(nèi)提取特征描述子。信息熵從圖像顯著性角度估計特征點及其周圍的信息,能獲得更多的圖像關(guān)鍵內(nèi)容,本文提出了基于局部熵的圖像特征描述方法。首先,在高斯差分空間(DOG)內(nèi)計算特征點的多層SIFT描述子,同時統(tǒng)計特征點在每層尺度上的局部熵,計算特征點在每層的熵值占所有層熵總和的百分比,利用所得百分比與每層描述子做乘積;然后,累加所有層描述子;最后,使用平方根算法得到最終局部熵特征描述子。通過與其他描述子的對比實驗結(jié)果可知,本文提出的局部圖像描述方法在精確-召回率、平均均勻準確度和正確匹配數(shù)方面具有強魯棒性。
[Abstract]:Using the traditional feature description method SIFT to describe the image features on a single scale will lose some important information, which will affect the correct matching results of the image. In order to solve this problem, the feature descriptors are extracted in multi-scale fuzzy space. Information entropy estimates the information of feature points and their surroundings from the point of view of image significance, and can obtain more key contents of image. In this paper, an image feature description method based on local entropy is proposed. Firstly, the multi-layer SIFT descriptors of feature points are calculated in Gaussian difference space (DOG), and the local entropy of feature points on each layer scale is counted, and the entropy value of feature points in each layer is calculated as a percentage of the total entropy of all layers, and the product of the obtained percentage and each layer narrator is made by using the obtained percentage; then, all layer descriptors are accumulated; finally, the final local entropy feature descriptors are obtained by using square root algorithm. The experimental results show that the local image description method proposed in this paper is robust in terms of accuracy-recall, average uniform accuracy and correct matching number.
【作者單位】： 吉林大學計算機科學與技術(shù)學院;吉林交通職業(yè)技術(shù)學院電子信息學院;
【基金】：國家自然科學基金項目(61101155) 吉林省發(fā)展和改革委員會產(chǎn)業(yè)創(chuàng)新專項項目(2016C035)
【分類號】：TP391.41
[Abstract]:Using the traditional feature description method SIFT to describe the image features on a single scale will lose some important information, which will affect the correct matching results of the image. In order to solve this problem, the feature descriptors are extracted in multi-scale fuzzy space. Information entropy estimates the information of feature points and their surroundings from the point of view of image significance, and can obtain more key contents of image. In this paper, an image feature description method based on local entropy is proposed. Firstly, the multi-layer SIFT descriptors of feature points are calculated in Gaussian difference space (DOG), and the local entropy of feature points on each layer scale is counted, and the entropy value of feature points in each layer is calculated as a percentage of the total entropy of all layers, and the product of the obtained percentage and each layer narrator is made by using the obtained percentage; then, all layer descriptors are accumulated; finally, the final local entropy feature descriptors are obtained by using square root algorithm. The experimental results show that the local image description method proposed in this paper is robust in terms of accuracy-recall, average uniform accuracy and correct matching number.
【作者單位】： 吉林大學計算機科學與技術(shù)學院;吉林交通職業(yè)技術(shù)學院電子信息學院;
【基金】：國家自然科學基金項目(61101155) 吉林省發(fā)展和改革委員會產(chǎn)業(yè)創(chuàng)新專項項目(2016C035)
【分類號】：TP391.41
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