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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1 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE Semi-supervised learning 2 2 Segmentation by RecognitionLabeling by RecognitionSemi-supervised Learning 1. [1, 2] 1 (i) (ii) (iii) (iv) NEOCR [3] Street View House Numbers Dataset [4] (ii) (iv) 1: (ii) (iii) (iv) Semi-supervised learning [5, 6] 2 2. Semi-supervised learning 2. 1 [7] [8 11] [12] [2] [2] [2] [2] 1 1 1
2 3: 2: 2. 2 Semi-supervised learning Semi-supervised learning [5,6] Semi-supervised learning Self-training [13] [14] Self-training Self-training Semi-supervised learning Self-training Nearest Neighbor Nearest Neighbor (I) PCA-SIFT [15] Dense Sampling (II) [16] d nn d 2nn d nn d 2nn < t d (1) t d 3 (III)Reference Point(RP) [17] RP RP RP (IV) (i)rp d c RP (ii) RP n c (iii) RP x y (V) 4 2 x d x x d x d x d x x S x S x S x = S d x dx y 2
3 S dx dy S d y S dy S d x dx d x 4: d y 5: (I) (II) (III) (IV) (III) (V) (II)(IV) Reliability Check Dense Sampling ID 3. 1 (1) 1/N i N i i ID Reliability Check Reliability Check s 1 2 s 2 t l < s 2 s 1 < t u (2) t l t s 1 (1) Street View House Numbers Dataset Street View House Numbers Dataset [4] Full Numbers Full Numbers train 3
4 70 60 # labeled data : 100 # labeled data : 500 # labeled data : 1000 Recall [%] Precision [%] (a) 100 (b) : 1Recall-Precision 7: [pixels] ,000 extra 1,000 t d d c n c RecallPrecision d c n c [18] , Reliability Check ,000 train extra 8: 1 test t d t l t u (2) a. b. (1) c. (1) d. b d a c b d (1) b d a c ab cd 4
5 1: 2-1 a. b. c. d. [%] [%] ,360 46,834 30,624 33,531 [%] : 3 [%] 77.5 [%] ,037 [%] Reliability Check 9(a) 1,000 10,000 1,000 (Ground Truth) Correct CheckCorrect Check (1) Reliability Check Ground Truth Correct Check Correct Check Reliability Check 9(b) 9(a) Reliability Check Recognition Rate [%] # of Retrained Data Ground Truth Correct Check Proposed Method # of Unlabeled Data per Class (a) Ground Truth Correct Check Proposed Method # of Unlabeled Data per Class (b) 9:
6 Recognition Rate [%] # of Labeled Data: 10 # of Labeled Data: # of Labeled Data: 500 # of Labeled Data: 100 # of Labeled Data: # of Labeled Data + Unlabeled Data per Class 10: Reliability Check Reliability Check (2) 2 2 train 1,000 extra 5,000 13,215 test t d d c n c t l t u % 4,037 73% 2,947 2 Reliability Check Reliability Check 5. Reliabiilty Check JST CREST [1] M. Iwamura, T. Tsuji, and K. Kise, Memory-based recognition of camera-captured characters, Proc. DAS, [2] M. Iwamura, T. Kobayashi, and K. Kise, Recognition of multiple characters in a scene image using arrangement of local features, Proc. ICDAR, [3] A.D. Robert Nagy and K. Meyer-Wegener, NEOCR: A configurable dataset for natural image text recognition, Proc. CBDAR, [4] Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A.Y. Ng, Reading digits in natural images with unsupervised feature learning, NIPS Workshop on Deep Learning and Unsupervised Feature Learning, [5] O. Chapelle, B. Schölkopf, and A. Zien eds., Semisupervised learning, Cambridge, MIT Press, Sept [6] X. Zhu and A.B. Goldberg, Introduction to semi-supervised learning, Morgan and Claypool Publishers, Sept [7] J.-J. Lee, P.-H. Lee, S.-W. Lee, A.L. Yuille, and C. Koch, Adaboost for text detection in natural scene, Proc. IC- DAR, [8] B. Epshtein, E. Ofek, and Y. Wexler, Detecting text in natural scenes with stroke width transform, Proc. CVPR, [9] P. Sanketi, H. Shen, and J.M. Coughlan, Localizing blurry and low-resolution text in natural images, Proc. IEEE Workshop on Applications of Computer Vision, [10] C. Yao, Z. Tu, and Y. Ma, Detecting texts of arbitrary orientations in natural images, Proc. CVPR, [11] L. Neumann and J. Matas, Real-time scene text localization and recognition, Proc. CVPR, [12] Y.-F. Pan, X. Hou, and C.-L. Liu, A hybrid approach to detect and localize texts in natural scene images, IEEE Trans. on Image Processing, vol.20, no.3, pp , March [13] M. Tsukada, M. Iwamura, and K. Kise, Expanding recognizable distorted characters using self-corrective recognition, Proc. DAS, [14] V. Frinken, M. Baumgartner, A. Fischer, and H. Bunke, Semi-supervised learning for cursive handwriting recognition using keyword spotting, Proc. ICFHR, [15] Y. Ke and R. Sukthankar, PCA-SIFT: a more distinctive representation for local image descriptors, Proc. CVPR, pp , [16] pp.73 78PRMU Feb [17] M. Klinkigt and K. Kise, Using a reference point for local configuration of sift-like features for object recognition with serious background clutter, IPSJ Trans. on Computer Vision and Applications, vol.3, pp , Dec [18] C. Wolf and J.-M. Jolion, Object count/area graphs for the evaluation of object detection and segmentation algorithms, IJDAR, vol.8, no.4,
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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