從視訊中選取特徵點對於影像分析，例如動作辨識，提供了強力且具有代表性的資訊。然而目前提出的方法中，都有著些許問題例如多數的方法僅使用區域性的資訊來做偵測，或是花費了大量的運算時間。基於這個原因，我們在此論文提出了一個新的方法來克服這些問題。

在這篇論文中，我們提出了一個可以從視訊中偵測和選取特徵點的方法。我們可以使用這些特徵點來表示一個影片中主要的運動資訊。本方法由三個部份所構成，首先我們先將影像序列利用主成分分析技術分解成空間和時間兩個主要的子空間來表示，之後我們分別對這兩個子空間的二維影像及一維向量基底進行特徵點偵測，最後我們用一個特徵衡量方法來選取在這些偵測到的所有三維特徵點組和中最具有代表性的點來當作代表此視訊的特徵。我們將此方法應用到人臉表
情辨識的問題，實驗結果顯示此方法可以提供精確及快速的辨識結果。

Spatio-temporal interest points in video provide a compact and representative information for the video analysis such as motion recognition. Most of the previous
spatio-temporal interest point detection methods used only local information and their detection results are not robust. Recently, a new interest point detector based on
applying NNMF (Non-Negative Matrix Factorization) on a video sequence to obtain the spatial subspace matrix and temporal coefficient matrix was proposed and proved
to significantly improve the accuracy of several motion recognition tasks. However,this NNMF-based interest point detector takes a considerable amount of execution time.
In this thesis, we propose a PCA-based algorithm for spatio-temporal interest point detection and selection from a video sequence. The proposed algorithm is composed of three stages. At the first stage, we factorize the video sequence into spatial subspace matrix and the corresponding temporal coefficient matrix. Then we detect the interest points on the 2D spatial subspace eigen-images and the 1D temporal coefficient vectors separately. Finally, we apply a saliency measure to select representative interest points from all the pairings of the detected points. Experimental
results on facial expression recognition show the motion classification based on the proposed interest point detector based on PCA-based matrix factorization can provide
satisfactory accuracy and its computational speed is much faster than the NNMF-based interest point detector.

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