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研究生: 劉琦韻
Liu, Chi-Yun
論文名稱: 基於擴展式賽局的區域比對影像共分割方法
An Extensive Form Game Based Matching Method for Image Co-segmentation
指導教授: 張隆紋
口試委員: 王聖智
劉庭祿
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2012
畢業學年度: 100
語文別: 英文
論文頁數: 43
中文關鍵詞: 賽局理論影像共分割比對
外文關鍵詞: Game theory, Image co-segmentation, Matching
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  • 影像共分割在近年來被廣泛討論著,其定義是同步的將一組影像中相同或相似的區域切割出來。有別於過去的一些方法,在本篇論文中,我們試圖以不同角度去思考並解決此問題。由於影像共分割的重點在於如何去判斷出影像所共同具有的區域,我們提出一個基於擴展式賽局的區域比對演算法來解決這個問題。
    在這個比對賽局中,每張影像被視為理性的玩家,玩家的策略集被定義為他們所具有的區域,每位玩家意圖去找出一些具有較高比對分數的配對以得到較高的收益。我粉所提出的方法首先抽取顏色和材質特徵,以所提出的比對賽局來找出那些具有較高比對分數的區域配對,透過區域比對的方式,來定義出影像所共同具有的前景。在此我們假設那些共同前景的配對相較於其他配對有著較高的比對分數,因此透過適當的選取門檻值,我們可以將各張影像中的共同前景定位出來。接著我們應用Graph Cut的方式來完成分割,將分割結果收斂到一個令人滿意的程度。實驗結果顯示,相較於其他現有的方法,我們所提出方法在主觀及客觀方面皆展現出較高的效能。


    Co-segmentation is widely discussed in recent years. The main goal of co-segmentation is simultaneously segmenting the same or similar objects from a set of images. Unlike previous methods, in this paper, we think about this problem from different angles. Due to the central part of co-segmentation is to find the common regions among images, we propose an extensive-form game based matching algorithm to solve this problem.
    In the proposed matching game, each image represents a rational player, and their strategies are their superpixels. Players purpose to find some matched pairs with higher matching score in order to get higher payoff. In our method, according to the color and texture feature, we utilize the proposed matching game to find matched pairs with higher matching score. Here we assume that the matched pairs between common regions usually have higher score than others. So we can locate the common foreground in each image by choosing a proper threshold. Then we apply graph cut to segment the foreground regions of each images independently. We show the superior performance of our method in comparison with other state-of-the-art techniques on some realistic dataset.

    Chapter 1 Introduction Chapter 2 Related Work Chapter 3 Proposed Method 3.1 Superpixel Segmentation 3.2 Feature Extraction 3.3 Extensive Form Game Based Matching Algorithm 3.4 Graph Cut Chapter 4 Experiment Results Chapter 5 Conclusion Reference

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