投打畫面是棒球比賽中事件的起點，精確的定位投打畫面成為分析棒球比賽影片內容最重要的步驟。現有的場景偵測的作法需要繁複的訓練過程或是人工標記的動作，對於未知的影片資料既定的規則可能會失效，缺少泛用性。在這篇論文中，我們提出了不需經過人工標籤或是訓練過程的投打畫面自動偵測方式，給定一段棒球比賽影片，經由影片分段、段落分群、群落選擇及影格分類四個步驟後，即可偵測並列出該影片中由投打畫面所組成的片段。本系統結合了影格相似度分析、貝氏訊息準則和亂度等方式做偵測，改善了現有作法的缺點，實驗的結果顯示出本論文提出的作法具有很可靠的準確率。

Pitching segments are the starting points of every baseball event. Locating pitching segments accurately becomes a critical step in content analysis of baseball game video. However, existing scene detecting method either need complicate training process or labor effort labeling, and it might fail to deal with unseen data. In this paper, we present an unsupervised method to address the above problems. Given a video clip, the proposed method constructs clusters of video segments and ranks them to build a pitching model through four steps: video segment, segment clustering, clustering selection, and frame classification. The system which combines similarity analysis, Bayesian information criterion, and entropy for modeling and detecting pitching scene resolves the defect of existing methods. Our experiments also demonstrate a promising result of the proposed method.

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