在真實世界的資料庫裡，例如生物資訊庫，數位音樂庫及人體動作庫等都可能存有隱藏的結構（latent structure）。這類的隱藏資訊通常有助於我們對原始資料的建模分析以及後續的應用。本論文提出一種新的內容摘要法，此方法能自動地發掘動作庫（即動作截取器捕捉大量運動資料而成的資料庫）中之隱藏結構並藉以提出合理的摘要。我們所提出的方法不僅可以捕捉到一段動作的局部特性（亦即相鄰畫格的主要差異），同時還能找到此段動作的整體結構（亦即動作中含有次要動作）。
然而直接對複雜的高維動作資料去作處理不但不容易而且相當耗費時間的成本。因此本方法首先對動作資訊以分群的技術（clustering）將動作分段並對每一段動作予以符號化（symbolization）。於是整段動作即可視為一個動作字串（motion string）。我們再設法以重複類型（repeating pattern）的分析來找出此動作字串中之隱藏的結構。隨著隱藏結構的發現，我們用以產生上下文無關文法（context free grammar）來簡潔明瞭化的階層式表達此動作。然後利用本方法所產生出來的文法，可以用來架構出此段動作之靜態摘要（summary）和動態濃縮（skimming）動畫。最後我們可以因此透過聚焦的部份（highlight）或摘要的段落（summary sequence）來快速地瀏覽一段動畫；也可以使用少數挑選出來的關鍵畫格來做說明它；以及讓我們從它粹取出來具有意義的子段落中達到了解與體會它的動作。

Latent structures exist in sequences ranging from biosequence, music, human motion to various real-world data. Such hidden information provides a more flexible way to model and use the original input data. In the thesis, we present a method to discover the latent structure and then to infer the hidden knowledge of the given skeletal animation (i.e. motion capture data). The proposed approach not only captures the local characteristics (i.e. the variation between two consecutive frames) of a given motion sequence, but also discovers its global structure (i.e. the sub-actions of an action).
It is difficult and time-consuming to mine the high-dimensional data directly. Therefore, given a motion sequence, we first convert it into motion units called symbols through a cluster-based symbolization process. Consequently, the input sequence could be regarded as a motion string. Then our method discovers the latent structure based on an analysis of the repeating patterns of the motion string. Once the latent structure has been discovered, a grammar could be generated for obtaining a compact and hierarchical representation of the given sequence. Using the grammar that is produced by our method, we can provide both animation skimming and summary of the given animation. Therefore, an animation can be browsed by its highlights or summary sequence quickly; an animation can be illustrated with few selected keyframes; and we can understand and describe an animation via its meaningful sub-sequences.

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