FoodLog [3] TADAproject [4] Google Goggles 1 Kumar [5] () Leaf snap Maruyama [6] 3 Lee [7] Yu [8] Gist SVM Active Query Sensing(AQS)
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1 DEIM Forum 213 D3-4 食事認識を用いたモバイル食事管理システム 河野 憲之 柳井 啓司 電気通信大学 電気通信学部 情報工学科 東京都調布市調布ヶ丘 電気通信大学 大学院情報理工学研究科 総合情報学専攻 東京都調布市調布ヶ丘 あらまし 近年 スマートフォンの性能が大きく向上している そこで 本研究では 通常サーバにデータを送り 画 像処理をする部分をスマートフォン上でリアルタイムに実行することにより 通信コストのかからない ネットワー クに依存しない食事管理システムを提案する 5 種類の料理に対して 背景を含まない料理の領域が与えられたと き 候補を 5 つ提示し 81.4%の認識精度であった また バックグラウンドでは料理の領域の補正を行い さらに 認 識を誤った場合を考慮し ユーザに料理のある方向を提示する ユーザに提示する料理の方向は 認識する領域から 料理が 15%ずれていた場合 角度差 ±2 以内に 31.8% ±4 以内に 5.3%の精度で 25%ずれていた場合 角度差 ±2 以内に 34.5% ±4 以内に 54.2%の精度であることを確認した キーワード モバイル 食事認識 食事管理 ユーザインタラクティブ 1. は じ め に 近年 健康志向の高まりによりスマートフォンなどのモバイ ルデバイスから食事記録をとることのできるシステムが多く現 れるようになった 一般的な食事管理システムの記録方法は テキスト入力や階層型メニューによる選択などが挙げられるが 入力に手間がかかり 継続した利用が難しい また スマートフォンの普及により それに伴いスマートフォ ンの性能も大きく向上し スマートフォン上で以前より計算コ ストの高い処理をすることが可能になった スマートフォンか ら画像処理を利用する一般的なシステムは スマートフォンを 通信手段として利用することが多いが 通信コストがかかり ネットワークにも依存する そこで本研究では スマートフォン上で食事認識をリアルタ (a) システムを食事にかざす イムに行うことにより ネットワークに依存しないモバイル食 事管理システムを提案する 図 1 は提案システムのイメージで ㄆ ᯝ ある 2. 関 連 研 究 ಙ㢗ᗘ 㣗 ಖᏑ 㣗 㡿ᇦ䛾᪉ 食事認識は食べ物に決まった形はなく 同じカテゴリ内で あっても視覚的変化が大きいため 難しいタスクである 松田 ら [1] は 円検出 JSEG DPM により食事領域推定後 SIFT HOG Gabor カラーヒストグラムにより 1 種類の食事画 像に対して複数品の分類に取り組んだ Yang ら [2] は 画素間 の距離や角度等 材料の位置関係を特徴量とする手法により ファストフードの分類に取り組んだ 本研究でも 食事認識を 行い 料理を分類して結果上位をユーザに提示する 食事管理システムとしては 一般的な画像認識を用いない場 合は 料理データベースの拡大が容易であるが 手動による入 力のため手間が多く 継続した利用が難しい 画像認識を用い た食事管理システムでは 食事画像からバランス推定をし そ 㣗 㑅ᢥ ㄆ 㡿ᇦ (b) システムの認識画面 図 1 提案システムのイメージ 㔞ධຊ
2 FoodLog [3] TADAproject [4] Google Goggles 1 Kumar [5] () Leaf snap Maruyama [6] 3 Lee [7] Yu [8] Gist SVM Active Query Sensing(AQS) http:// 8 2 () Google Maps [9] 4 5 Web Web 4. Gabor HOGPHOGSURF Bosch [1] Hoashi [11] [12] SVM RGB HSV La*b* RGB HSV RGB La*b*
3 㣗 双 厭 厸 去 㣗 ㄆ 㣗 㑅 ᢥ 㡿 ᇦ ෆ 䜢 ㄆ 㣗 ಖ Ꮡ (a) 閲覧 (1 日ごとに閲覧 4 群点数に基づき 5 つ星で評価する ) (b) 詳細情報 (1 食分の栄養素を表示する ) (c) 食事画像 (記録した食事画像を表示する ) (d) Map(位置情報を付けると Google Maps 上に表示する ) 䝞 䝷 䞁 䝇 ㄆ 㘓 (e) 食の傾向 (ユーザの食事の傾向を表示する ) 図2 使用の流れ (f) 詳細情報 (Web にアップロードして表示する ) 図3 記録閲覧
4 [13] RGB 4 2k + 1( < = k < = 16, k : ) Gabor Gabor HOG HOG [14] PHOG PHOG [15] SURF SURF [16] dense sampling Bag-of-Features 5 1 soft [17] 3 soft hard kd-tree 4. 2 SVM 1-vs-rest K(x, z) = x z SVM x f(x) x i α i b f(x) = = = N α i K(x, x i ) + b (1) i=1 N α i x x i + b i=1 N α i x i x + b i=1 = w x + b (2) N SVM liblinear [18] BoF SVM explicit embedding kernel feature maps 4. 3 Kernel feature maps Kernel feature maps SVM SVM SVM Vedaldi [19] Hellingerχ 2 intersectionjensen-shannon(js) φ φ ω = κ ω xe i ω,log x (3) χ 2 χ 2 φ e iω log x xsech(πω) 3 L1 [19] L1 PHOGSURF Perronnin [2] Hellinger L GrabCut GrabCut GrabCut [21] GMM GrabCut GrabCut 2 6 GrabCut
5 4. 5 SURF-BoF SVM Window SVM ESS(Efficient Subwindow Search) [22] SVM BoF 2 w 2 w w = w + + w (4) SVM w + w O(1) soft w B B (B 2) B = 2x (3 < = x < = 6, x : ) 1 SVM [1] 5 1 6,781 Ground Truth %1 1, = N 4 3 x%(x=1,15,2,25) = y 1 5 SURF-BoF soft χ 2 χ 2
6 BoF χ 2 SURF-BoF 2 SURF SURF GrabCut SURF RGB+SURF RGB+SURF,GrabCut %5 81.6% %5 81.4% % % %5 1.1% 1 4.1%5 3.1% % 25% %, GrabCut 25%, GrabCut % hard soft hard soft ±2 ±4 ±6 ±8 ±1 ±12 ±14 ±16 ±18 15% hard soft hard ±2 29.7%±4 47.5%soft ±2 31.8%±4 5.3% ±4 soft soft x%(x=1,15,2,25) 9 25%±2 34.5% ±4 54.2%
7 % 15% 2% 25% 5. 2 ±2 ±4 ±6 ±8 ±1 ±12 ±14 ±16 ±18 x% (x=1,15,2,25) Galaxy NoteII(1.6GHz Quad Core Android4.1) SURF SURF GrabCut 1 [sec] sec or 5 () [sec] SURF 4 SURF 4 GrabCut 2 SVM kd-tree SURF-BoF.3sec SURF or
8 % 15% ±2 31.8%±4 5.3% 25% ±2 34.5%±4 54.2% 2 3 [1],,.. D, Vol. J95-D, No. 8, pp , 212. [2] S. Yang, M. Chen, D. Pomerleau, and R. Sukthankar. Food recognition using statistics of pairwise local features. In Proc. of IEEE Computer Vision and Pattern Recognition, 21. [3] K. Kitamura, T. Yamasaki, and K. Aizawa. Foodlog: Capture, analysis and retrieval of personal food images via web. In Proc. of ACM Multimedia Workshop on Multimedia for Cooking and Eating Activities, pp. 23 3, 29. [4] A. Mariappan, M. Bosch, F. Zhu, C.J. Boushey, D.A. Kerr, D.S. Ebert, and E.J. Delp. Personal dietary assessment using mobile devices. In Proc. of the IS&T/SPIE Conference on Computational Imaging VII, Vol. 7246, pp. 7246Z Z 12, 29. [5] N. Kumar, P. Belhumeur, A. Biswas, D. Jacobs, W. Kress, I. Lopez, and J. Soares. Leafsnap: A computer vision system for automatic plant species identification. In Proc. of European Conference on Computer Vision, 212. [6] T. Maruyama, Y. Kawano, and K. Yanai. Real-time mobile recipe recommendation system using food ingredient recognition. In Proc. of ACM Multimedia Workshop on Interactive Multimedia on Mobile and Portable Devices, pp , 212. [7] T. Lee and S. Soatto. Learning and matching multiscale template descriptors for real-time detection, localization and tracking. In Proc. of IEEE Computer Vision and Pattern Recognition, 211. [8] F.X. Yu, R. Ji, and S.F. Chang. Active query sensing for mobile location search. In Proc. of the 19th ACM International Conference on Multimedia, pp. 3 12, 211. [9].., 22/5. [1] M. Bosch, F. Zhu, N. Khanna, C.J. Boushey, and E.J. Delp. Combining global and local features for food identification in dietary assessment. In Proc. of IEEE International Conference on Image Processing, pp IEEE, 211. [11] H. Hoashi and K. Yanai. Image recognition of 85 food categories by feature fusion. In Proc. of The second Workshop on Multimedia for Cooking and Eating Activities, 21. [12],,.. (MIRU212), 212. [13] J. Huang, S.R. Kumar, M. Mitra, W.J. Zhu, and R. Zabih. Image indexing using color correlograms. In Proc. of IEEE Computer Vision and Pattern Recognition, pp , [14] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. of IEEE Computer Vision and Pattern Recognition, Vol. 1, pp IEEE, 25. [15] A. Bosch, A. Zisserman, and X. Munoz. Representing shape with a spatial pyramid kernel. In Proc. of the 6th ACM International Conference on Image and Video Retrieval, pp , 27. [16] Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. Speeded-up robust features (SURF). Computer Vision and Image Understanding, Vol. 11, No. 3, pp , 28. [17] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In Proc. of IEEE Computer Vision and Pattern Recognition, pp. 1 8, 28. [18] R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research, Vol. 9, pp , 28. [19] A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 3, pp , 212. [2] F. Perronnin, J. Sánchez, and Y. Liu. Large-scale image categorization with explicit data embedding. In Proc. of IEEE Computer Vision and Pattern Recognition, pp , 21. [21] C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. In Proc. of ACM SIGGRAPH, pp , 24. [22] C. H. Lampert, M. B. Blaschko, and T. Hofmann. Beyond sliding windows: Object localization by efficient subwindow search. In Proc. of IEEE Computer Vision and Pattern Recognition, 28.
情報処理学会研究報告 プレートマッチングによりリアルタイムに物体検出や追跡 を行うアプリケーションが提案されるなど近年モバイルと 画像認識の研究が盛んに行われている 本研究では視覚的 変化の大きい料理に対してスマートフォンの計算資源のみ を用いてリアルタイムに料理認識を行う ユーザインタラクティブな
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DEIM Forum 2012 B5-1 テレビ番組からの位置情報付き旅行映像データベースの自動構築 向井 康貴 柳井 啓司 電気通信大学 電気通信学部 情報工学科 182 8585 東京都調布市調布ヶ丘 1-5-1 E-mail: mukai-y@mmcsuecacjp, yanai@csuecacjp あらまし 本研究では 録画したテレビ番組の内容に関連した場所を推定し 地図上に配置することにより録画した
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1 1 1, Extraction of Transmitted Light using Parallel High-frequency Illumination Kenichiro Tanaka 1 Yasuhiro Mukaigawa 1 Yasushi Yagi 1 Abstract: We propose a new sharpening method of transmitted scene
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