基於聯合字典學習與辨識器的局部保留  K-SVD 於物件辨識之研究

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姓名

趙家豪(Jia-hao Zhao) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於聯合字典學習與辨識器的局部保留 K-SVD 於物件辨識之研究
(A Study on Locality Preserving K-SVD via Joint Dictionary and Classifier Learning for Object Recognition)

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摘要(中)

電腦視覺是一門研究如何令機器理解圖像的科學，隨著現在電腦速度的改進，處理圖像這種大規模資料的應用漸漸地扮演了舉足輕重的腳色，而物件辨識(Object Recognition)，就是其中一種相當熱門的技術，為了達到精準辨識的目的，從傳統的支持向量機(SVM)、稀疏表示分類器(SRC)以及DK-SVD(Discriminative K-SVD)，皆能獲得一定的效果。
基於DK-SVD，本論文提出一種結合局部保留投影(LPP)概念的LP-KSVD，在訓練的目標函示中加入局部保留的限制，使得訓練字典的同時能保留資料間局部的特性，並同時訓練出可用於物件辨識的線性分類器。除此之外，我們也提出將局部保留的目標式做為一種新的特徵，在一筆新的輸入訊號進來時，也能夠直接獲得額外的局部保留資訊；我們也使用核化方法將訓練資料投射至高維特徵空間以增強字典的辨識及重建能力並且提升系統彈性。
在實驗上使用Caltech101與15Scene資料庫，其結果顯示LP-KSVD在辨識率上有更好的表現。

摘要(英)

Computer vision is a field that understanding images. Along with advances in computer performance, processing large-scale data becomes more common than before. Object Recognition is one of the popular computer vision technology. In order to enhance the recognition performance, we can use the traditional SVM, SRC and DK-SVD.
In this paper, we present locality preserving K-SVD based on DK-KSVD. Adding the locality preserving term into the objective function to preserve the neighborhood structure of the data set. The locality preserving term can be used as a new feature. We can gain additional locality information for new input signals. We also apply kernel method on the dictionary learning which efficiently strengthen the reconstructive and discriminative ability.
Experiments on Caltech101 and 15Scene databases indicate that the proposed method achieve good recognition performance.

關鍵字(中)

★ K-SVD
★ DK-SVD
★ 聯合字典學習
★ 局部保留
★ 物件辨識

關鍵字(英)

★ K-SVD
★ DK-SVD
★ Joint Dictionary Learning
★ Locality Preserving
★ Object Recognition

論文目次

摘要 ii
Abstract iii
章節目次 iv
圖目錄 vii
表目錄 vii
第一章緒論 1
1-1 前言 1
1-2 研究動機與目的 2
1-3 論文架構與章節概要 3
第二章相關研究及文獻探討 4
2-1 特徵處理 4
2-1-1 SPM 4
2-1-2 LPP 6
2-2 字典學習 7
2-2-1 基於L0-Norm的字典學習 7
2-2-2 OMP 7
2-2-3 K-SVD 10
2-3 字典學習與辨識器 13
2-3-1 SRC 13
2-3-2 聯合字典學習 15
2-3-3 Discriminative K-SVD (DK-SVD) 16
2-3-4 Label consistent K-SVD 18
第三章物件辨識系統 20
3-1 系統架構 20
3-2 局部保留投影 21
3-2-1 LP-KSVD1 21
3-2-2 LP-KSVD2 23
3-2-3 Sparse Coding LP-KSVD1 24
3-2-4 Sparse Coding LP-KSVD2 24
3-2-5 Discriminant LP-KSVD 26
3-2-6 Kernel LP-KSVD1 28
3-2-7 Kernel LP-KSVD2 29
3-2-8 Combined with LC-KSVD 31
3-2-9 LP-KSVD的初始化 32
第四章實驗結果 33
4-1 Caltech101實驗設置與環境 33
4-1-1 不同方法比較 35
4-1-2 訓練資料數量比較 37
4-1-3 字典大小比較 38
4-2 Fifteen Scene Categories實驗設置與環境 39
4-2-1 訓練資料數量比較 40
4-2-2 字典大小比較 43
第五章結論及未來研究方向 44
參考文獻 45

參考文獻

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指導教授

王家慶(Jia-Ching Wang)

審核日期

2015-8-24

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