LBP 2 LBP 2. 2 Local Binary Pattern Local Binary pattern(lbp) [6] R
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1 DEIM Forum 24 F5-4 Local Binary Pattern {tera,kida}@ist.hokudai.ac.jp Local Binary Pattern (LBP) LBP 3 3 LBP LBP improved LBP uniform LBP.. Local Binary Pattern, Gradient Local Auto-Correlations,,,, [] [2] Shift Invariant Feature Transform (SIFT) [3] Speeded-up Robust Feature (SURF) [4] SIFT SURF visual word Bag-of-Keypoints [5] Local Binary Pattern (LBP) [6] Higher Order Local Auto-Correlations (HLAC) [7] HLAC Gradient Local Auto-Correlations (GLAC) [8] Normal Local Auto-Correlations (NLAC) [8] LBP LBP [9] [] [9]SIFT SURF GLAC NLAC LBP GLAC 5 LBP LBP improved LBP [] uniform LBP []. 2 LBP rotated LBP [3] LBP LBP OpenCV 2 OpenCV Haar-like [4] LBP
2 LBP 2 LBP 2. 2 Local Binary Pattern Local Binary pattern(lbp) [6] R P P LBP P,R = s(g p g c)2 p s(x) = p= { (x > = ) (x < ) g c g p R = 3 3 P 8 R = P 6 LBP g c g p LBP P,R 2 P LBP LBP P R R = P = 8 LBP R = P = 8 LBP LBP 8, 2 8 = 2 LBP P,R LBP LBP LBP 3 LBP improved LBP [] uniform uniform LBP [] LBP P,R rotated LBP [3] 2. 3 Gradient Local Auto-Correlations Gradient Local Auto-Correlations (GLAC) [8] HLAC [7]
3 (a) (b) 6 LBP 4 gradient orientation vector 7 D + 4 D (, δx δy )t n = ( δx )2 + ( δy )2 θ = arctan(, ) δx δy GLAC 4 D 2 4 f( R D ) gradient orientation vector (G-O ) N GLAC R(d,..., d N, a,..., a N ) = min[n(r), n(r + a ),..., n(r + a N )] I f d (r)f d (r + a )...f dn (r + a N )dr f d G-O f d a i 5 N N = N = GLAC GLAC : R(d ) = r I n(r)f d (r) : R(d, d, a ) = min[n(r), n(r + a )]f d (r)f d (r + a ) r I GLAC 5 D GLAC 5 GLAC R = 2P = 6 LBP P,R GLAC 3 3 LBP 5 5 GLAC G-O D = = 5 dglac GLAC 3 3 dglac G-O D = 8 GLAC R = P = 8 LBP 2 I I(x, y) n θ 2 n = + 2 δx δy θ = arctan( δx, δy ) = I(x +, y) I(x, y) δx = I(x, y + ) I(x, y ) δy GLAC GLAC LBP dglac R = 2P = 6 LBP P,R LBP 2 6 6(a) 6 [9] 6 6(b)
4 表 顔画像分類の性能 図 8 AT&T データセット AT&T MIT CBCL Proposed 9.77% % LBP 88.76% 99.58% improved LBP 87.5% % uniform LBP 85.% 99.53% けているもの かけていないもの等 様々な顔画像が含まれて いる MIT CBCL データセットは図 9 のような顔画像のデータ セットであり 人のグレースケール画像が各 2 枚ずつ 計 2 枚の画像からなる 画像はクラスごとにサイズが正規化 されておらず 様々な角度から撮影されている これらのデータセットを用いて以下のような条件で画像分類 の実験を行った 各クラスからランダムに半分の画像を選択し それを 図 9 MIT CBCL データセット 学習画像とする うな場合も考えられる 本論文では 予備調査の結果 精度に 2 残りの半分の画像を分類画像とする 大きな差が確認されなかったため図 6(a) に統一した これによ 3 学習画像 及び分類画像から特徴量を抽出する り通常 R = 2 P = 6 としたときの LBP では 2 6 = 次元になるのに対し 5 5 の範囲の情報を 26 = 64 次元で表 4 学習画像の特徴量を多クラス分類が可能な線形 SVM により学習する 5 生成された分類器に分類画像の特徴量を入力する すことができる 最後に 次の GLAC を LBP による 64 個のパターンで分割 6 出力された結果が正しいクラスに属しているか否かを することで特徴量の記述を行う 次の GLAC は 8 方向 (8 次 調べ 正しいクラスであれば正解とする 元) で特徴づけられているので dglac は 5 個のパターン AT&T データセットでは学習画像 分類画像はそれぞれ 5 枚 で表現される 特徴量は 次の GLAC の特徴量を基準として MIT CBCL データセットでは学習画像 分類画像はそれぞれ いるので LBP のようなパターンベースの特徴量ではなく注目 枚となる このような実験を 回行い その平均を求め 画素の画素値の重みによって算出されるパワースペクトルベー ることで 精度を求めた 提案手法は予備調査の結果 特徴量 スの特徴量となる 最終的に dglac の参照点は図 7 のよう を正規化しないほうが良い結果が得られていたため 正規化は になる 算出される特徴量 D は 次の GLAC 特徴量 R(d) と 行っていない 提案手法に対する比較手法としては LBP の他 LBP6,2 を用いて以下のように表せる に improved LBP と uniform LBP を用いた uniform LBP では注目画素の周囲の範囲を 3 3 から 7 7 まで変化させ DLBP6,2,d = R(d) たうえで 最良の結果を用いており AT&T データセットでは 以上により 注目画素の周囲 5 5 の範囲から疎に参照点を 選択し勾配情報を用いて特徴量を算出することで 5 次元の 555 次元を MIT CBCL データセットでは 次元の特徴量 を使用している 実 験 結 果 は 表 の よ う に なった AT&T デ ー タ セット 特徴量として記述される では 提案手法が 9.77%であるのに対し 既存手法では 4. 分 類 実 験 LBP が 88.76% improved LBP が 87.5% unform LBP 提案手法の有効性を調べるために顔画像を用いた画像分類の 注 2 実験を行う 実験には AT&T データセット と MIT CBCL データセット 注 3 を使用した AT&T データセットは図 8 のような顔画像のデータセット であり 4 人のグレースケール画像が各 枚ずつ 計 4 枚 の画像からなる 全ての画像は 92 ピクセルで固定され ている また データセットには目が開いているもの 目が閉 じているもの 笑っているもの 笑っていないもの 眼鏡をか が 85.%という結果となり 提案手法が既存手法より優れ ていることが確認できる MIT CBCL データセットにおい ても提案手法が %であるのに対し LBP が 99.58% improved LBP が % uniform LBP が 99.53%となり いずれにおいても提案手法が優れている結果となった これは 提案手法が既存手法に対し より多くに情報を特徴量に含んで いる点 そして勾配情報を用いていることからそれぞれの人間 における固有の顔の情報を取得できているからであると考えら れる 注 2 html 注 3 facerecognition-database.html 5. お わ り に 本稿において LBP の参照する範囲を広げることで特徴量
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情報処理学会研究報告 い認識率を示す事が出来なかったと報告している 視覚特徴量としては SIFT や SURF のような局所的な 領域から特徴量を抽出する方法がある [4] [5] これらの 特徴量とフローベクトルを使いダイナミックなシーンの分 類を行う手法が提案されている しかし これらの画像特
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