Cette thèse traite dela mise en correspondance de séquences appliquée au word spotting (localisation de motsclés dans des images de documents sans en interpréter le contenu). De nombreux algorithmes existent mais très peu d’entre eux ont été évalués dans ce contexte. Nous commençons donc par une étude comparative de ces méthodes sur plusieurs bases d’images de documents historiques. Nous proposons ensuite un nouvel algorithme réunissant la plupart des possibilités offertes séparément dans les autres algorithmes. Ainsi, le FSM (Flexible Sequence Matching) permet de réaliser des correspondances multiples sans considérer des éléments bruités dans la séquence cible, qu’ils se situent au début, à la fin ou bien au coeur de la correspondance. Nous étendons ensuite ces possibilités à la séquence requête en définissant un nouvel algorithme (ESC : Examplary Sequence Cardinality). Finalement, nous proposons une méthode d’appariement alternative utilisant une mise en correspondance inexacte de chaines de codes (shape code) décrivant les mots. / This thesis deals with sequence matching techniques, applied to word spotting (locating keywords in document images without interpreting the content). Several sequence matching techniques exist in the literature but very few of them have been evaluated in the context of word spotting. This thesis begins by a comparative study of these methods for word spotting on several datasets of historical images. After analyzing these approaches, we then propose a new algorithm, called as Flexible Sequence Matching (FSM) which combines most of the advantages offered separately by several other previously explored sequence matching algorithms. Thus, FSM is able to skip outliers from target sequence, which can be present at the beginning, at the end or in the middle of the target sequence. Moreover it can perform one-to-one, one-to-many and many-to-one correspondences between query and target sequence without considering noisy elements in the target sequence. We then also extend these characteristics to the query sequence by defining a new algorithm (ESC : Examplary Sequence Cardinality). Finally, we propose an alternative word matching technique by using an inexact chain codes (shape code), describing the words.

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通訊技術的演化與智慧型手機的普及，改變了人際溝通的方式與手機的應用情境，在此變動快速的行動運算時代，欲研究探討使用者的行為模式，必須建立一個包含硬體、軟體與使用者社群的實驗平台，以量化的數據補強質性的觀察，準此，本論文將以現有之平台為基礎，強化其功能與易用性，方便其他研究者觀察資料的概況，並擷取符合某些條件之資料，此外，我們採用3-gram之應用程式序列，作為行為模式(behavior pattern)之特徵定義，配合不同的應用程式被使用之頻率，在相似度比較上進行不同比重的加權，根據實驗結果，可大致對使用者進行初步的分類，亦可利用此指標，針對已分類過的使用者更進一步探討之間的歧異程度。 / The rapid evolution of information technology and prevalence of smart-phones have changed the way people communicate. To effectively observe and investigate user behavior in this new era of mobile computing, an experimental platform that consists of hardware devices, software applications and user groups is essential. In this thesis, we enhance and extend the functions of a user log collection and analysis system to facilitate quick overview of the recorded data and allow flexible query/extraction of desired data segments for further processing. In addition, we employ 3-gram app log sequence as the main feature to characterize user behavior. A similarity measure that takes into account the relative app usage frequency has been defined to compare and classify users and their usage patterns. Experimental results indicate that this measure can effectively distinguish users of different traits given enough time period of observation.