【摘要】：提出了一種建立在數(shù)據(jù)顆粒引力基礎(chǔ)上的圖像分類方法,此方法使用的數(shù)據(jù)顆粒質(zhì)量是圖像的特征(如圖像的分形維)。對于每一類別訓(xùn)練數(shù)據(jù)顆粒集,圖像特征采用訓(xùn)練數(shù)據(jù)顆粒集中幾幅圖像特征的均值m_i,用訓(xùn)練數(shù)據(jù)顆粒集的像幅數(shù)w_i作為它的權(quán)重,那么第i個訓(xùn)練數(shù)據(jù)顆粒集的質(zhì)量為w_im_i,而待檢驗數(shù)據(jù)顆粒是原子數(shù)據(jù)顆粒,其質(zhì)量為1。假定待檢驗一幅圖像數(shù)據(jù)顆粒j的特征為t_(m_j),那么第i個訓(xùn)練數(shù)據(jù)顆粒集與待檢驗一幅圖像的數(shù)據(jù)顆粒j之間的距離為|m_i-t_(m_j)|。假定有3種不同類別的圖像,從各類別中取出一部分圖像組成3類訓(xùn)練數(shù)據(jù)顆粒集,求得每類數(shù)據(jù)顆粒集特征的均值和一幅待檢驗數(shù)據(jù)顆粒的特征值,按公式計算每類數(shù)據(jù)顆粒集對待檢驗數(shù)據(jù)顆粒的引力,3個引力中具有最大引力的類別即為待檢驗數(shù)據(jù)顆粒的類別。實驗結(jié)果表明,基于數(shù)據(jù)引力的圖像分類方法具有一定的優(yōu)勢。
[Abstract]:An image classification method based on the gravity of data particles is proposed. The quality of the data particles used in this method is the feature of the image (such as the fractal dimension of the image). For each type of training data particle set, the image feature is based on the average value of several image features in the training data particle set, and the image amplitude of the training data particle set is used as its weight. Then the mass of the first training data particle set is WSTIM _ I _ s, while the data particle to be tested is atomic data particle, and its mass is 1. 5%. Assuming that the character of the image data particle j is t _ (maugj), the distance between the first training data particle set and the data particle j of an image to be examined is mStuff i-tj. Assuming that there are three different classes of images, a portion of the images are taken from each category to form a set of three kinds of training data particles, and the mean value of the feature of each class of data particle set and the eigenvalue of a piece of data particle to be tested are obtained. According to the formula, the gravitation of each kind of data particle set towards test data particle is calculated. The class of three kinds of gravity with maximum gravity is the class of data particle to be tested. Experimental results show that the image classification method based on data gravity has some advantages.
【作者單位】： 武漢大學(xué)深圳研究院;武漢大學(xué)遙感信息工程學(xué)院;武漢大學(xué)電子信息學(xué)院;
【基金】：深圳市基礎(chǔ)科研項目(JCYJ20150422150029095)~~
【分類號】：O314;TP391.41
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本文編號：2146519