特徵向量抽取 的英文怎麼說
中文拼音 [tèzhǐxiàngliángchōuqǔ]
特徵向量抽取
英文
feature vector extraction- 特 : Ⅰ形容詞（特殊; 超出一般） particular; special; exceptional; unusual Ⅱ副詞1 （特別） especially; v...
- 徵 : 名詞[音樂] （古代五音之一 相當于簡譜的「5」） a note of the ancient chinese five tone scale corre...
- 量 : 量動1. （度量） measure 2. （估量） estimate; size up
- 抽 : 動詞1 （把夾在中間的東西拉出; 提取） take out （from in between） 2 （從全部里取出一部分; 騰出） ...
- 取 : Ⅰ動詞1 （拿到身邊） take; get; fetch 2 （得到; 招致） aim at; seek 3 （採取; 選取） adopt; assume...
- 特徵 : characteristic; feature; properties; aspect; trait
- 抽取 : abstract
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Firstly, we extract the profile features of human face and non - human face from training images ( including human training images and non - human training images ). then, put them into support vector machines ( svm ) to train and classify those features, then use the training results to detect human faces
該方法是一種統計學的方法，從圖像訓練集（包括人臉訓練集和非人臉訓練集）中分別抽取出人臉和非人臉的輪廓特徵，然後送入支持向量機進行訓練和分類，將訓練結果用於人臉檢測。Firstly, to perform pca or lda on basis of such high - dimensional image vectors is a time - consuming process. secondly, the high dimensionality usually leads to singularity of the within - class covariance matrix, which is a trouble for calculation of fisher optimal discriminant vectors
這樣就從根本上避免了在高維的圖像向量空間內構造散布矩陣並計算特徵向量的困難，大幅度地降低了特徵抽取過程所耗費的計算量。The conventional principal component analysis ( pca ) and fisher linear discriminant analysis ( lda ) are based on vectors. that is to say, if we use them to deal with the image recognition problem, the first step is to transform original image matrices into same dimensional vectors, and then rely on these vectors to evaluate the covariance matrix and to determine the projector
所提出的這兩種方法的共同特點是，在進行圖像特徵抽取時，不需要事先將圖像矩陣轉化為高維的圖像向量，而是直接利用圖像矩陣本身構造圖像散布矩陣，然後基於這些散布矩陣進行主分量分析與線性鑒別分析。
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