(VKIR) VKIR VKIR DCT (R) (G) (B) Ward DCT i
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1 24 Region-Based Image Retrieval using Color Histogram Feature
2 (VKIR) VKIR VKIR DCT (R) (G) (B) Ward DCT i
3 Abstract Region-Based Image Retrieval using Color Histogram Feature Yuki SHINOHARA Visual-key image retrieval (VKIR) is a region-based image retrieval and uses average, variance of pixels as color features. VKIR also uses texture feature and shape feature. Precision of VKIR, however, is affected mainly by color features. In this research, we use color histogram feature for VKIR and compare with conventional average, variance of colors, and DCT coeffcient feature. We use 64 colors histogram for VKIR and then the dimension of feature vectors is 64. Indexing is performed by Ward clustering algorithm using 64-dimensional color features among image Sub-regions. All sub-images are clustered to 20 clusters and centroid image of each cluster is a visual key. Five subjects choose 2 visual-key in order to retrieve 10 variations of images. We compare the precision and recall of image retrieval by color histogram feature and conventional color features. key words color-histogram,visual-key Image Retrieval ii
4 Text-Based Image Retrival Cotent-Based Image Retrival Region-Based Image Retrival Ward iii
5 A 24 iv
6 F A A A A A v
7 A A A A A A A A vi
8 1 1.1 Google Yahoo! Google Yahoo!... (RBIR) (VKIR). ( ). VKIR VKIR VKIR DCT 1
9 2 2.1 TBIR(Text-Based Image Retrival) CBIR(Cotent-Based Image Retrival) TBIR CBIR TBIR CBIR Yahoo! Google Text-Based Image Retrival Text-Based Image Retrival(TBIR) TBIR. 2
10 Cotent-Based Image Retrival CBIR CBIR CBIR Region-Based Image Retrival RBIR CBIR RBIR RBIR RBIR 3
11 2.2 Image Retrieval 画像検索 TBIR(Text-Based Image Retrieval) テキストデータ CBIR(Content-Based Image Retrieval) 画像の特徴 RBIR(Region-Based Image Retrieval ) 分割画像の特徴 ビジュアルキー型画像検索 部分領域画像検索
12 L*a*b* [1] 9 L*a*b*. JPEG YCrCb DCT 14 [2]. YCrCb ( Y) ( CrCb). RGB. 5
13 (R) (G) (B) 2.3 [3] 2.3 6
14 = 色カラー画像 64 色のカラー画像 減色 2.4 RGB 4 64 RGB=(58,150,238) (Red No,Green No,Blue No)=(0,2,3)
15 通りの輝度値 R G B 輝度値を4 通りにした場合の各クラス中央値 色番号 ピクセル (R, G,B) RGB 8+8+8ビット 1677 万色 減色 2+2+2ビット 64 色 ピクセル数 Color-Histogram 色番号 64 色
16 3 PHP Apache SQLite. Web Web. VKIR PHP. SQLite 構築 Web サーバ Apache 開発言語 PHP データベース SQLite 画像データベース PHP Web ブラウザ Web サーバ Apache データベース SQLite 3.1 9
17 元画像 ピクセルに拡大縮小 4 4 枚に分割
18 a b c d
19 3.2 cluster cluster d cluster c a b Ward Ward Ward G E(G) E(G) = X m(g) 2 (3.1) X G X m(g) G i, G j E(G i, G j ) E(G i, G j ) = E(G i G j ) E(G i ) E(G j ) (3.2) 12
20 Ward Ward 枚に分割 クラスタリング結果 クラスタ 1 クラスタ 2 クラスタ 5 クラスタ 3 クラスタ 11 クラスタ 4 クラスタ
21 被験者の名前 検索目的画像 1.jpg を求める場合 20 番のビジュアルキーを選択
22
23 4 4.1 L*a*b* Pixel. YCrCb DCT. Pixel DCT ArtExplosion ArtExplosion = = (4.1) (4.2) 16
24 Pixel 14% 26% DCT 10% 22% 19% 40% F Pixel 0.18 DCT F
25 Better pixel DCT Color-Histogram 適合率 0.14 ± ± ±0.08 再現率 0.26 ± ± ± Better pixel DCT Color-Histogram 4.3 F 18
26 % RGB
27 5. RGB. RGB F. Pixel 5 14 F 7. DCT 9 18 F L*a*b* YCrCb. 20
28 4,
29 3 3 22
30 [1] M.Serata,et al, Designing Image System with the Concept of Visual Keys, JACIII,10(2),pp ,2006 [2], DCT, 19,2007 [3]
31 A.. pixel L*a*b*. DCT YCrCb DCT. Color-Histogram RGB. A.1 24
32 Pixel A A A 適合率 A B C D E 被験者 A 再現率 A B C D E 被験者 A.3 25
33 Pixel 適合率 画像番号 A 再現率 画像番号 A.5 26
34 DCT B D B D 0.16 適合率 A B C D E 被験者 A 再現率 A B C D E 被験者 A.7 27
35 DCT 9 9 DCT 適合率 画像番号 A 再現率 画像番号 A.9 28
36 Color-Histogram B D E B D E 適合率 A B C D E 被験者 A.10 再現率 A B C D E 被験者 A.11 29
37 Color-Histogram 適合率 画像番号 A 再現率 画像番号 A.13 30
24 Region-Based Image Retrieval using Fuzzy Clustering
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20 200302878 1 5 2 7 2.1........................... 7 2.1.1 TBIR............................... 7 2.1.2 CBIR............................... 9 2.2............................... 10 2.2.1 2...............................
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