Microsoft PowerPoint - cvim_harada pptx
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1 1
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3 Flickr reaches 6 billion photos on 1 Aug,
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7 位 LSVRC 位 LSVRC 位 Localization Car Car Categorization Car
8 1. neck brace 2. bullet train 3. potter's wheel 4. seat belt 5. barbell 1. mountain bike 2. hartebeest 3. yurt 4. bighorn 5. coho 1. brown bear 2. otter 3. hippopotamus 4. raccoon 5. deerhound 1. volleyball 2. bittern 3. shower curtain 4. crane 5. suspension bridge 1. mask 2. ski mask 3. jack-o'-lantern 4. jellyfish 5. teddy bear 1. toilet seat 2. scanner 3. hard disc 4. scale 5. backpack 1. baseball player 2. racket, racquet 3. solar dish 4. trimaran 5. paddle 1. aircraft carrier 2. paddle 3. bullfrog 4. water ouzel 5. mantis 8
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10 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. 10 David J.C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press 2003.
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14 S. Vijayanarasimhan and K. Grauman. Large Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds. In CVPR, 2011.
15 S. Vijayanarasimhan and K. Grauman. Large Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds. In CVPR, HOG deformation LLC+max pooling No deformation NIPS2010
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18 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,
19 Attributes and Classification 20
20 21
21 S. Dhar, V. Ordonez, and T. L Berg. High Level Describable Attributes for Predicting Aesthetics and Interestingness. CVPR,
22 S. Dhar, V. Ordonez, and T. L Berg. High Level Describable Attributes for Predicting Aesthetics and Interestingness. CVPR,
23 24
24 M. Douze, A. Ramisa, and C. Schmid. Combining attributes and Fisher vectors for efficient image retrieval. CVPR,
25 26
26 D. Parikh and K. Grauman. Relative Attributes. In ICCV,
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30 31
31 Deng et al., CVPR
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34 d 2 d 3 d m d 1 d k d j d N 1) Input Image d m 2) Detection 3) Description p( d; θ) d N d 2 d 1 d k x f (θ) d j d 3 4) Local descriptors in feature space 5) PDF estimation 6) Feature vector 35
35 d 2 d 1 d m Local descriptors in feature space d k d N d j d 3 Descriptor matching Codebook Global feature # of anchor points: large # of anchor points: small Computational complexity: large Computational complexity: small SVM KNN Naïve Bayes Nearest Neighbor Graph Matching Kernel Bag of Visual Words Gaussian Mixture Model ScSPM, Super Vector, LLC Fisher Vector HLAC GLC Global Gaussian 36
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37 H. Zhang, A. C. Berg, M. Maire, and J. Malik. SVM KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition. In CVPR,
38 T. Tuytelaars, M. Fritz, K. Saenko, and T. Darrell. The NBNN kernel. In ICCV,
39 40
40 O. Duchenne, A. Joulin and J. Ponce. A Graph Matching Kernel for Object Categorization. ICCV,
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43 w 3 w 1 w 4 R d w 2 44
44 Bag of Visual Words Kernel codebook d 1 w1 w2 w3 w4 [ f kc ( x )] i k K j 1 exp 2 exp d 2 i w d j k 2 w k 2 w1 w2 w3 w4 d 1 d 2 w1 w2 w3 w4 d 1 w1 w2 w3 w4 d 2 d 3 w1 w2 w3 w4 w 1 d 2 w 3 d 3 w1 w2 w3 w4 d 3 d 4 w1 w2 w3 w4 w 2 w1 w2 w3 w4 d 4 f BoW 1 N fbow( xi ) N i 1 d 4 w 4 f kc 1 N fkc ( xi ) N i 1 w1 w2 w3 w4 w1 w2 w3 w4 45
45 Image Local descriptors in feature space PDF estimation 46
46 Generative approach Image Local descriptors in feature space PDF estimation Fisher Kernel Feature vector Fisher Vector Discriminative classifier F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. CVPR, Discriminative approach Classifier e.g., SVMs Category 47
47 48
48 49
49 50
50 51
51 net.org/challenges/lsvrc/2010/ilsvrc2010_xrce.pdf 52
52 K p( x; ) w Ν ( x;, k 1 k k k ) ˆμ 1 Image Local descriptors in feature space U U μ ~ ( ) ( ) 1/ k wk ( k ) 2 μˆ k μˆ GMM ( U ) ˆ k k N i1 μˆ K ˆμ 2 Means of components N i1 ( k) x i i ( k) i μ~ 1 μ~ 2 ( X ) μ ~ K GMM supervectors 53
53 54 N i i N i i i U k k k x k 1 1 ) ( ) ( ) ( ˆ ˆ μ N i i i U k U k N i i i U k N i k U k k U k U k k x k w N x k i w w 1 2 1/ ) ( ) ( 1 2 1/ ) ( 1 ) ( 2 1/ ) ( ) ( ) ( ) ( 1 ) ( ) ( ) ( ˆ ) ( ~ μ μ 0 N i i Nw k k 1 ) ( N i k i k i k i k w N g 1 2 1/, ) ( 1 μ x N. Inoue and K. Shinoda. A Fast MAP Adaptation Technique for GMMsupervector based Video Semantic Indexing. ACM Multimedia, 2011.
54 55 Asymmetric Distance Computation
55 H. Jegou, M. Douze, C. Schmid, and P. Perez. Aggregating local descriptors into a compact image representation. CVPR,
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64 net.org/challenges/lsvrc/2010/ilsvrc2010_nec UIUC.pdf 65
65 net.org/challenges/lsvrc/2010/ilsvrc2010_nec UIUC.pdf 66
66 J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. CVPR,
67
68 H. Nakayama, T. Harada, and Y. Kuniyoshi. Dense Sampling Low Level Statistics of Local Features. In CIVR, GMM Single Gaussian 69
69 H. Nakayama, T. Harada, and Y. Kuniyoshi. Dense Sampling Low Level Statistics of Local Features. In CIVR,
70 H. Nakayama, T. Harada, and Y. Kuniyoshi. Global Gaussian Approach for Scene Categorization Using Information Geometry. In CVPR, Image 1 Local descriptor space Feature vector Feature vector Local descriptor space Image 2 (1) x (2) x Similarity? ( j) x (i) x (2) x (k ) x (1) x Manifold
71 H. Nakayama, T. Harada, and Y. Kuniyoshi. Global Gaussian Approach for Scene Categorization Using Information Geometry. In CVPR,
72 H. Nakayama, T. Harada, and Y. Kuniyoshi. Global Gaussian Approach for Scene Categorization Using Information Geometry. In CVPR,
73 Super Vector Coding VLAD GMM + Bag of Visual Words Fisher Vector Sparse Coding Global Gaussian Local Coordinate Coding Bag of Visual Words Locality constrained Linear Coding 74
74 75
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76 J. Sanchez, and F. Perronnin. High Dimensional Signature Compression for Large Scale Image Classification. In CVPR, 2011.
77 78
78 識別機 CPU 識別機 識別機 識別機 CPU 識別機 識別機 識別機 CPU 識別機 識別機 データデータデータ データ データ データ HDD データ データ HDD HDD 79
79 D dim D/N dim D/N dim w 3 w 3 2^K w 3 w 3 w 1 w 4 w 1 w 4 w 1 w 4 w 1 w 4 w 2 w 2 w 2 w 2 NK/D [bit/dim] NK/D [bit/dim] NK/D [bit/dim] NK/D [bit/dim] 80
80 81
81 net.org/challenges/lsvrc/2011/ilsvrc11.pdf 82
82 83
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 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#
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,a),b),,,,,,,, (DNN),,,, (CNN),,.,,,,,,,,,,,,,,,,,, [], [6], [7], [], [3]., [8], [0], [7],,,, Tohoku University a) omokawa@vision.is.tohoku.ac.jp b) okatani@vision.is.tohoku.ac.jp, [3],, (DNN), DNN, [3],
IPSJ SIG Technical Report Vol.2010-CVIM-170 No /1/ Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Ta
1 1 1 1 2 1. Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Takayuki Okatani 1 and Koichiro Deguchi 1 This paper presents a method for recognizing the pose of a wire harness
Duplicate Near Duplicate Intact Partial Copy Original Image Near Partial Copy Near Partial Copy with a background (a) (b) 2 1 [6] SIFT SIFT SIF
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Partial Copy Detection of Line Drawings from a Large-Scale Database Weihan Sun, Koichi Kise Graduate School of Engineering, Osaka Prefecture University E-mail: sunweihan@m.cs.osakafu-u.ac.jp, kise@cs.osakafu-u.ac.jp
bag-of-words bag-of-keypoints Web bagof-keypoints Nearest Neighbor SVM Nearest Neighbor SIFT Nearest Neighbor bag-of-keypoints Nearest Neighbor SVM 84
Bag-of-Keypoints Web G.Csurka bag-of-keypoints Web Bag-of-keypoints SVM 5.% Web Image Classification with Bag-of-Keypoints Taichi joutou and Keiji yanai Recently, need for generic image recognition is
Microsoft PowerPoint - IBIS_harada _2.pptx
1 beach, water, people, kauai, tree ocean, coral, fish, angelfish, reefs Retrieving by flight 2 Image Annotation Results on Corel5K birds, booby, flight, rocks, water buildings, ships, bridge, flag, sky
IPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/10 Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing
Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing number of HOG Features based on Real AdaBoost Chika Matsushima, 1 Yuji Yamauchi, 1 Takayoshi Yamashita 1, 2 and
(b) BoF codeword codeword BoF (c) BoF Fergus Weber [11] Weber [12] Weber Fergus BoF (b) Fergus [13] Fergus 2. Fergus 2. 1 Fergus [3]
![(b) BoF codeword codeword BoF (c) BoF Fergus Weber [11] Weber [12] Weber Fergus BoF (b) Fergus [13] Fergus 2. Fergus 2. 1 Fergus [3]](/thumbs/49/25500502.jpg "(b) BoF codeword codeword BoF (c) BoF Fergus Weber [11] Weber [12] Weber Fergus BoF (b) Fergus [13] Fergus 2. Fergus 2. 1 Fergus [3]")
* A Multimodal Constellation Model for Generic Object Recognition Yasunori KAMIYA, Tomokazu TAKAHASHI,IchiroIDE, and Hiroshi MURASE Bag of Features (BoF) BoF EM 1. [1] Part-based Graduate School of Information
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3,a) 3 3 ( ) DB 3D DB 2D,,,, PnP(Perspective n-point), Ransac. DB [] [2] 3 DB Web Web DB Web NTT NTT Media Intelligence Laboratories, - Hikarinooka Yokosuka-Shi, Kanagawa 239-0847 Japan a) yabushita.hiroko@lab.ntt.co.jp
Microsoft PowerPoint - IBIS_harada_
2 Intelligence for Real World Recognition Natural Language Processing Database Pattern Recognition Information Theory Computer Vision Machine Learning Data Mining Robotics Cognitive Science Parallel Computing
(MIRU2010) Geometric Context Randomized Trees Geometric Context Rand
(MIRU2010) 2010 7 Geometric Context Randomized Trees 487-8501 1200 E-mail: {fukuta,ky}@vision.cs.chubu.ac.jp, hf@cs.chubu.ac.jp Geometric Context Randomized Trees 10 3, Geometric Context, Abstract Image
4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
![4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q](/thumbs/92/110416683.jpg "4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q")
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
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DEIM Forum 2016 C5-1 182-8585 1-5-1 E-mail: saitoh-ryoh@uec.ac.jp, terada.minoru@uec.ac.jp Twitter,, Twitter,,, Bag of Words, Latent Semantic Indexing,.,,,, Twitter,, Twitter,, 1. SNS, SNS Twitter 1,,,
2 Fig D human model. 1 Fig. 1 The flow of proposed method )9)10) 2.2 3)4)7) 5)11)12)13)14) TOF 1 3 TOF 3 2 c 2011 Information
1 1 2 TOF 2 (D-HOG HOG) Recall D-HOG 0.07 HOG 0.16 Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata 1 and Hironobu Fujiyoshi 1 A method for estimating the pose of a human from
3: 2: 2. 2 Semi-supervised learning Semi-supervised learning [5,6] Semi-supervised learning Self-training [13] [14] Self-training Self-training Semi-s
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 599-8531 1-1 E-mail: tsukada@m.cs.osakafu-u.ac.jp, {masa,kise}@cs.osakafu-u.ac.jp Semi-supervised learning
Dynamic Time Warping( DTW DTW 30 k-d tree Forebes [1] 2. DTW[2] DTW DTW DTW Forbes[1] k-d tree DTW Hsu[3] DTW Zhu[4] K-SVD Sun[5] Self-S
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東京大学大学院情報理工学系研究科創造情報学専攻中山研究室中山英樹 どちらがハヤブサでしょう? http://plaza.rakuten.co.jp http://birds.mints.ne.jp 2 どちらがハヤブサでしょう? タカ ハヤブサ http://plaza.rakuten.co.jp http://birds.mints.ne.jp 3 車種はなんでしょう? 4 車種はなんでしょう?
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1 1 2,,.,.,,, SIFT.,,. Pitching Motion Analysis Using Image Processing Shinya Kasahara, 1 Issei Fujishiro 1 and Yoshio Ohno 2 At present, analysis of pitching motion from baseball videos is timeconsuming
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26 Discrimination of abnormal breath sound by using the features of breath sound 1150313 ,,,,,,,,,,,,, i Abstract Discrimination of abnormal breath sound by using the features of breath sound SATO Ryo
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