以影片內容為基底的視訊影片檢索之研究

Abstract

在這篇論文中，我們將會提出一個以影片內容為基底的影片檢索的方法，且這個方法不需對每段影片去找出具有代表性的影像。和那些方法不同的地方在於，我們的方法能將整段影片的資訊都使用到。我們所提出的方法是利用”主要的色彩矩量”和“主要的顏色”這兩個觀念去對每段影片擷取特徵。要從一段影片中取出”主要的色彩矩量”，首先將這段影片中的每一張影像分割成許多塊，然後對每一塊計算它的色彩矩量，再以這些矩量的值為基準將所有的塊分成很多群，每一群的中心向量就是這段影片的一個主要色彩矩量。要從一段影片中取出它的主要顏色，就是將這段影片中的每一點依據它們顏色的值去做分群，每一群裡面所有顏色的平均值就視為這段影片中的一個主要顏色。在對每段影片擷取出它們的特徵向量後，我們將會對主要的色彩矩量和主要的顏色分別提出一個計算兩段不同影片之間相似度的方法。然而，因為沒有一種方法是適合所有種類的影片，我們將會提出一個演算法，它能夠依據使用者所給的回應，自動的找出一個最適合的方法。

In this paper, a content-based video retrieval method without using key-frame will be proposed. Unlike the key-frame based approach, the proposed method uses the whole information of a shot instead of selecting several key-frames to represent a shot. This method is based on the concept of the primitives of color moments and the dominant colors to extract features. To extract the primitives of color moments, first each frame in a shot is divided into several blocks. Then, the color moments of all blocks are extracted and clustered into several classes. The mean moments of each class are considered as a primitive of the shot. To extract the dominant colors, all pixel’s color in a shot are clustered into several classes, and the center of the colors in each class is treat as a dominant color. After extracting the feature vectors for each shot, we will propose two measures to compute the similarity between two different shots using primitives of color moments and dominant colors as features, respectively. Furthermore, since there is no feature that is proper for all kinds of shots, a relevance feedback algorithm is also provided to automatically determine the best method according to the user’s response.