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空間關係分類以及相似性量測之 範用結構 - PowerPoint PPT Presentation

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空間關係分類以及相似性量測之 範用結構

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A General Framework For Classification and Similarity Measure of Spatial Relationship

Symbol-Based
• 以文字當搜尋鍵值
• Google影像搜尋系統 :
• 缺點: 意義含糊、耗費大量人力註解

2

Content-Based Image Retrieval(CBIR)

Color Histogram

[Shamik2002, Saha S.K2004]

Reference Color Table Method

[S. C. Pei99]

Sobel Edge Detection

[S.E.Lotfy2000,Natalia2004]

Chain Code

[Chalechale2004, Heechan2005]

Multiple-Instance Learning (MIL)

{藍天、白雲、樹木、岩石、瀑布}

1. {藍天、白雲、樹木、岩石、瀑布}

2. {岩石、瀑布、河流}

3. {樹木、岩石、瀑布}

Concept = {岩石,瀑布}–{藍天,白雲,草原,岩石} ={瀑布}

4

MIL搜尋結果

3(+):

1(-):

RSS：Retrieval by Spatial Similarity

Positive Images

Retrieval Images

RSS

,

Ranking?

Feature Space?

,

(RSS)

SMR

SRC

[2001]

[97]

9DLT Matrix

SRR

[2001]

[91]

GPN Matrix

PN Matrix

[97]

[2001]

2d G-string

2d C-string

2d C+ string

[94]

2d string

2d string

[88]

[92]

2d B-string

2d Bε-string

SRR

MSR

[87]

[92]

[92]

[2005]

[2004]

WeiRe’s method

2d H-string

[88]

[2001]

2d+ string

2d PIR-string

[99]

SBA

[95]

Pointset

[91]

[2005]

13

推論出二維空間169(13*13)種空間關係

(1)

Vs.

(2)

Vs.

,

,

(3)

Vs.

SRU：空間關係宇集合

Positive Images

Negative Images

Retrieval Images

A

17

Positive Images:(P)

Retrieval Images:( R)

Negative Images:(N)

RSS

,

,

1. 空間關係之相似性:由特徵空間和距離計算

2.使用MIL產生的比重問題

Positive Images:

MIL

Proportion for Spatial Similarity :

Positive Images:

MIL

A:

C:

,

Negative Images:(N)

B:未被分類元素 =SRU-A-C

,

,

∈ B

∈ A

Positive Images:(P)

retrieval images:

,

vs.

Negative Images:(N)

,

• 可讓使用者能動態地歸結出有興趣之空間關係範圍，並計算出影像間之相似性以作為搜尋之依據
• 未來展望：

1.加入碎形編碼技術

2.加入其他特徵比對