Microsoft PowerPoint - IBIS_harada _2.pptx
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2 beach, water, people, kauai, tree ocean, coral, fish, angelfish, reefs Retrieving by flight 2
3 Image Annotation Results on Corel5K birds, booby, flight, rocks, water buildings, ships, bridge, flag, sky church, stone, buildings, chapel, people sky, people, closeup, statue, clouds buildings, water, city, light, night people, woman, indian, pots, baby cat, tiger, water, rocks, forest
4 4
5 Jet plane sky cat tiger forest tree beach people water oahu 5
6 Blob to Label Translation Model. Duygulu et al., CMRM. Jeon et al., Pixel to Label SML. Carneiro et al., Image From Carneiro et al., PAMI2007. From Duygulu et al., ECCV2002. Image X W pixel Image Instance to Label MBRM. Feng et al., JEC. Makadia et al., X W Lbl Label Concept to Label Lbl Label CorrLDA. Blei and Jordan, KCCA. Hardoon et al., CCA. Kurita et al., Z Concept obtained by image and label X Image W Label 6
7 Concept Learning and Image Annotation white bear river snow fox white Fox: 0.90 White:0.83 River:0.54 Bear: 0.54 Snow: 0.51 bear brown grass bird sky flight fox grass brown black bear river Image Concept feature space space 7
8 Head Mount Display displays labels of objects and scene with the image. Camera captures images, which the wearer is seeing now. Portable Computer recognizes the images quickly that the camera is capturing, and shows the results on HMD. Moreover, it accumulates the images and labels, and enables the wearer to search those images by labels. 8
9 AI Goggles 9
10 AI Goggles on iphone Project
11 AI Goggles on iphone prototype
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13 Demo déjà vu system 1 sec 16 byte/frame 1 day 16 byte x 1 fps x 60 sec x 60 min x 16 hour = 1 MB 1 year 1 MB x 365 = 365 MB 1 life 365 MB x 80 = 30 GB = 1 year iphone4s: 512MB = 1 life Mac Pro: 64GB
14 Your life is 2000 yen!
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16 S. J. Hwang, F. Sha, and K. Grauman. Sharing Features Between Objects and Their Attributes. CVPR, V. Ferrari and A. Zisserman. Learning visual attributes. In NIPS,
17 M. Rohrbach, M. Stark, and B. Schiele. Evaluating Knowledge Transfer and Zero Shot Learning in a Large Scale Setting. CVPR,
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19 The state of the world The gathered data The processed data w d r I ( W; D ) I ( W; R ) The data processing theorem states that data processing can only destroy information. 19 David J.C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press 2003.
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23 Deng et al., CVPR
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26 french fries mashed potato black olive face powder crab apple Granny Smith strawberry blueberry cranberry currant blackberry raspberry persimmon mul berry orange kumquat lemon grapefruit plum fig pineapple banana jackfruit cher ry grape custard apple durian mango elderberry guava litchi pomegranate quince kidney bean soy green pea chickpea chard lettuce cress spinach bell pepper pimento jalapeno cherry tomato parsnip turnip mustard bok choy head cabbage broccoli cauliflower brussels sprouts zucchini spaghetti squash acorn squash butternut squash cucumber artichoke asparagus green onion shallot leek cardoon celery mushroom pumpkin cliff lunar crater valley alp volcano promontory sandbar dune coral reef lakeside seashore geyser bakery juniper berry gourd acorn olive hip ear pumpkin seed sunflower seed coffee bean rapeseed corn buckeye bean peanut walnut cashew chestnut hazelnut coco nut pecan pistachio lentil pea peanut okra sunflower lesser celandine wood anemone blue columbine delphinium nigella calla lily sandwort pink baby s breath ice plant globe amaranth four o clock l Virginia i i spring beauty wallflower damask violet candytuft Iceland poppy prickly poppy oriental poppy celandine blue poppy Welsh poppy celandine poppy corydalis pearly everlasting strawflower yellow chamomile dusty miller tansy daisy common marigold China aster cornflower chrysanthemum mistflower など 26
27 フライドポテトはジャガイモのマッシュアップブラックオリーブブ白粉のカニリンゴグラニースミスのイチゴブルーベリークランベリーカラントブラックベリーラズベリーの柿桑オレンジキンカンレモングレープフルーツ梅図パイナップルバナナジャックフルーツ桜のブドウカスタードアップルをドリアンマンゴーニワトコグアバライチザクロカリンインゲン大豆グリーンピースのひよこ豆のフダンソウのレタスクレソンのほうれん草ピーマンピーマンハラペーニョミニトマトパースニップカブマスタードボクは菜ヘッドキャベツブロッコリーカリフラワー芽キャベツのズッキーニのスパゲティは スカッシュスカッシュドングリバタースカッシュキュウリはアーティチョークアスパラガスねぎエシャロットをネギカルドンのセロリマッシュルームカボチャの崖の月面のクレーターの谷アルプスの火山岬砂州の砂丘にサンゴ礁を湖畔海岸間欠泉のパン屋さんジュニパーベリーは ヒョウタンドングリオリーブヒップ耳カボチャの種ヒマワリの種コーヒー豆の菜種トウモロコシバックアイ豆ピーナッツクルミカシューナッツ栗ヘーゼルナッツココナッツピーカンナッツピスタチオ豆豆ピーナッツオクラヒマワリ低いクサノオウの木のクマノミブルーコロンバインデルフィニウムニゲラのカーラリリー sandwort ピンク赤ちゃんの呼吸アイスプラントの世界をアマランサス四 Oの ' クロックバージニア春の美しさの壁の花のダマスクバイオレットキャンディータフトアイスランドポピー厄介ポピーオリエンタルポピークサノオウ青いケシウェルシュポピークサノオウケシキケマン真珠のような永遠のストローフィールド黄色のカモミールダスティーミラーのヨモギギクにデイジーチェーン共通マリーゴールドエゾギクコーンフラワーキクキク科ヒヨドリバナ属の多年草のなど 27
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31 Input A dog is lying in the garden. summarize A dog is wearing a suit. A motorbike parked in a garden. A dog is wearing a suit. A fox is lying in a garden. A motorbike parked in a garden. Ab bear is riding on a horse. A bear is riding on a horse. A fox is lying in a garden. Concept space 31
32 Yoshitaka Ushiku, Tatsuya Harada, and Yasuo Kuniyoshi. Automatic Sentence Generation from Images. ACMMM, accepted,
33 Yoshitaka Ushiku, Tatsuya Harada, and Yasuo Kuniyoshi. Automatic Sentence Generation from Images. ACMMM, accepted,
34 NEC: LLC, SV XRCE: Fisher Vector NEC: HOG, LBP XRCE: SIFT, Color Local Descriptors Semi local feature Spatial Pyramid Image Feature Linear Classifier Linear SVMs Category 34
35 35
36 J. Yang, K. Yu, Y. Gong and T. Huang. Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification. CVPR,
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39 15 Scenes Caltech101
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41 w 3 w 1 w 4 R d w 2 41
42 Image Local descriptors in feature space PDF estimation 42
43 Generative approach Image Local ldescriptors in feature space Fisher Kernel Feature F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. CVPR, Discriminative approach Discriminative classifier Classifier PDF estimation vector e.g., SVMs Fisher Vector Category 43
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48 net.org/challenges/lsvrc/2010/ilsvrc2010_xrce.pdf pdf Bag of Visual Words (GMM) Fisher Vector 48
49 49
50 q(x) u(x) 50
51 net.org/challenges/lsvrc/2010/ilsvrc2010_xrce.pdf 51
52 net.org/challenges/lsvrc/2010/ilsvrc2010_xrce.pdf pdf 52
53 53 Asymmetric Distance Computation
54 H. Jegou, M. Douze, C. Schmid, and P. Perez. Aggregating local descriptors into a compact image representation. CVPR,
55 55
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63 J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. CVPR,
64 64
65 net.org/challenges/lsvrc/2010/ilsvrc2010_nec UIUC.pdf /hll / / / 65
66 66
67 net.org/challenges/lsvrc/2010/ilsvrc2010_nec UIUC.pdf / h /LSVRC/2010/ILSVRC
68 htt // i t / h ll /LSVRC/2010/ILSVRC2010 NEC UIUC net.org/challenges/lsvrc/2010/ilsvrc2010_nec UIUC.pdf 68
69 Super Vector Coding VLAD GMM + Bag of Visual Words Fisher Vector Sparse Coding Global Gaussian H. Nakayama, T. Harada, and Y. Kuniyoshi. Global Gaussian Approach for Scene Categorization Using Information Geometry. In CVPR, Local Coordinate Coding Bag of Visual Words Locality constrained t i Linear Coding 69
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71 O. Duchenne, A. Joulin and Jean Ponce. A Graph Matching Kernel for Object Categorization. ICCV,
72 72
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1 Flickr reached 5,000,000,000 photos on September 19, 2010. http://blog.flickr.net/en/2010/09/19/5000000000/ 2 http://www.flickr.com/photos/kullin/4999988381/ 3 http://twitter.com/randizuckerberg/status/22187407218577408#
Microsoft PowerPoint - SSII_harada pptx
The state of the world The gathered data The processed data w d r I( W; D) I( W; R) The data processing theorem states that data processing can only destroy information. David J.C. MacKay. Information
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1 2 Flickr reaches 6 billion photos on 1 Aug, 2011. http://www.flickr.com/photos/eon60/6000000000/ 3 4 http://www.dpchallenge.com/image.php?image_id=997702 5 6 http://www.image-net.org/challenges/lsvrc/2011/pascal_ilsvrc_2011.pptx
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