Microsoft PowerPoint - SSII_harada pptx
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3 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 Theory, Inference, and Learning Algorithms. Cambridge University Press 2003.
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6 Local Features, e.g. SIFT D. Lowe 12
7 d 2 d 3 d m d 1 d d j d N 1) Input Image d m 2) Detection 3) Description p( d; θ) d N d 2 d 1 d x f (θ) d j d 3 4) Local descriptors in feature space 5) PDF estimation 6) Feature vector
8 d 2 d 1 d m Local descriptors in feature space d d N d j d 3 Descriptor matching Codeboo 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
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10 Visual Words
11 w 3 w 1 w 4 R d w 2
12 Bag of Visual Words Kernel codeboo d 1 w1 w2 w3 w4 [ f c ( x )] i K j 1 exp 2 exp d 2 i w d j 2 w 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 c 1 N fc ( xi ) N i 1 w1 w2 w3 w4 w1 w2 w3 w4
13 Image Local descriptors in feature space PDF estimation
14 Generative approach Image Local descriptors in feature space PDF estimation Fisher Kernel Feature vector Fisher Vector Discriminative classifier F. Perronnin and C. Dance. Fisher ernels on visual vocabularies for image categorization. CVPR, Discriminative approach Classifier e.g., SVMs Category
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19 net.org/challenges/lsvrc/2010/ilsvrc2010_xrce.pdf
20 K p( x; ) w Ν ( x;, 1 ) ˆμ 1 Image Local descriptors in feature space U U μ ~ ( ) ( ) 1/ w ( ) 2 μˆ μˆ GMM ( U ) ˆ N i1 μˆ K ˆμ 2 Means of components N i1 ( ) x i i ( ) i μ~ 1 μ~ 2 ( X ) μ ~ K GMM supervectors
21 N i i N i i i U x 1 1 ) ( ) ( ) ( ˆ ˆ μ N i i i U U N i i i U N i U U U x w N x i w w 1 2 1/ ) ( ) ( 1 2 1/ ) ( 1 ) ( 2 1/ ) ( ) ( ) ( ) ( 1 ) ( ) ( ) ( ˆ ) ( ~ μ μ 0 N i i Nw 1 ) ( N i i i i 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.
22 Asymmetric Distance Computation
23 H. Jegou, M. Douze, C. Schmid, and P. Perez. Aggregating local descriptors into a compact image representation. CVPR, 2010.
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27 ( x) 0,,0 T,0, s,( x v),0, d 1dim d 1dim d 1dim T
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33 Max pooling Sparse coding Local features J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. CVPR, 2009.
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35 H. Naayama, T. Harada, and Y. Kuniyoshi. Dense Sampling Low Level Statistics of Local Features. In CIVR, GMM Single Gaussian
36 H. Naayama, T. Harada, and Y. Kuniyoshi. Dense Sampling Low Level Statistics of Local Features. In CIVR, 2009.
37 H. Naayama, 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 ( ) x (1) x Manifold
38 H. Naayama, T. Harada, and Y. Kuniyoshi. Global Gaussian Approach for Scene Categorization Using Information Geometry. In CVPR, 2010.
39 H. Naayama, T. Harada, and Y. Kuniyoshi. Global Gaussian Approach for Scene Categorization Using Information Geometry. In CVPR, 2010.
40 Super Vector Coding VLAD GMM + Bag of Visual Words Fisher Vector Sparse Coding Global Gaussian Bag of Visual Words Local Coordinate Coding Locality constrained Linear Coding
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43 J. Sanchez, and F. Perronnin. High Dimensional Signature Compression for Large Scale Image Classification. In CVPR, 2011.
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45 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
46 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]
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48 net.org/challenges/lsvrc/2011/ilsvrc11.pdf
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Microsoft PowerPoint - cvim_harada pptx
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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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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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
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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,,,
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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],
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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
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.... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov
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")
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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
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Microsoft PowerPoint - IBIS_harada _2.pptx
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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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180 Vol. 35 No. 3, pp.180 185, 2017 Image Recognition by Deep Learning Hironobu Fujiyoshi and Takayoshi Yamashita Chubu University 1. 1990 2000 Scale-Invariant Feature Transform SIFT Histogram of Oriented
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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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JSPE-54-04 Factor Analysis of Relationhsip between One's Visual Estimation and Three Dimensional Surface Roughness Properties on Belt Sanded Surface Motoyoshi HASEGAWA and Masatoshi SHIRAYAMA This paper
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Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
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