Interframe Coding of Global Image Signatures for Mobile Augmented Reality

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1 Interframe Coding of Global Image Signatures for Mobile Augmented Reality David Chen 1, Mina Makar 1,2, Andre Araujo 1, Bernd Girod 1 1 Department of Electrical Engineering, Stanford University 2 Qualcomm Inc. 1

2 Samsung Galaxy S3 Smartphone 2

3 On-Device Image Matching System Extract Local Features Generate Global Signature Match with Database Global Signatures Perform Geometric Verification Local Database 3

4 Hybrid Image Matching System Extract Local Features Generate Global Signature Match with Database Global Signatures Perform Geometric Verification Wireless Network Send compact interframe coded stream of global signatures in uplink Match with Database Global Signatures Local Database Send labels, local features, and global signatures for top-ranked database candidates in downlink 0.49 Remote Database 4

5 Outline Related work on feature compression Interframe coding of global signatures Selective codeword propagation Selective frame propagation Selective frame propagation + local search Global signature embedding Coding and retrieval results 5

6 Related Work on Feature Compression Random projections [Yeo, 2008] Local Image Features Global Image Signatures Transform coding [Chandrasekhar, 2009] Location histogram coding [Tsai, 2009] CHoG [Chandrasekhar, 2009] Bag of hash bits [He, 2011] Trace transform [Brasnett, 2007] Tree histogram coding [Chen, 2009] Residual vectors [Perronnin, 2010] [Jegou, 2010] [Chen, 2011] Temporally coherent keypoint detector [Makar, 2012] Descriptor and location predictive coding [Makar, 2012] [Baroffio, 2014] Our Current Work Intraframe Coding Interframe Coding 6

7 Temporally Coherent Keypoint Detection Makar et al., 2012 time t Detection (D) Frame Forward Propagation (FP) Frame 7

8 Generating Feature Residuals k = 3 8

9 Generating Feature Residuals k = 3 9

10 Residual Enhanced Visual Vector () Chen et al., k codewords Query Image Extract Local Features Regularize with Power Law Reduce Dimensions by PCA Quantize Feature 0 Descriptors Normalize 0 Feature Residuals Ranked List Database Signatures Normalize Correlation Scores Compute Weighted Correlations Binarize Components from Sign Perform Cell-Specific LDA 0.62 Probability Matching Images Non-matching Images Hamming Distance Weights Hamming Distance 10

11 D-Frame Interframe Coding of FP-Frame FP-Frame Keypoints Keypoints Keypoints D-Frame Extract Extract Extract Extract R 1 R 2 R n R n+1 S 1 Predictively Encode S 2 S n-1 Predictively Encode S 1 Wireless Network Previous Signatures S 2 Continuous stream of signatures Decode Signatures Compute Weighted Correlations S n Recognition results + Features for top candidates Database Signatures S n+1 Mobile Device Server 11

12 D-Frame Interframe Coding of FP-Frame FP-Frame Keypoints Keypoints Keypoints D-Frame Extract Extract Extract Extract R 1 R 2 R n R n+1 S 1 Predictively Encode S 2 S n-1 Predictively Encode S 1 S 2 S n S n+1 Extracted Signature U t,1 = 1 U t,2 = 1 U t,3 = 0 U t,4 = 1 U t,5 = 0 U t,6 = 1 U t,k = 1 R t,1 R t,2 R t,4 R t,6 R t,k Transmitted Signature V t,1 = 1 V t,2 = 1 V t,3 = 0 V t,4 = 1 V t,5 = 0 V t,6 = 1 V t,k = 1 S t,1 S t,2 S t,4 S t,6 S t,k 12

13 Frame t D-Frame Extracted Selective Codeword Propagation (SCP) U t,1 = 1 U t,2 = 1 U t,3 = 0 U t,4 = 1 U t,5 = 1 U t,6 = 1 U t,7 = 0 U t,8 = 1 R t,1 R t,2 R t,4 R t,5 R t,6 R t,8 Mobile Device Frame t+1 U t+1,1 = 1 U t+1,2 = 1 U t+1,3 = 1 U t+1,4 = 1 U t+1,5 = 0 U t+1,6 = 1 U t+1,7 = 0 U t+1,8 = 1 FP-Frame Extracted R t+1,1 R t+1,2 R t+1,3 R t+1,4 R t+1,6 R t+1,8 Frame t+1 FP-Frame Sent Frame t D-Frame Received Frame t+1 FP-Frame Received AND AND AND AND AND AND AND AND V t+1,1 = 1 V t+1,2 = 1 V t+1,3 = 0 V t+1,4 = 1 V t+1,5 = 0 V t+1,6 = 1 V t+1,7 = 0 V t+1,8 = 1 S t+1,1 S t+1,2 S t+1,4 S t+1,6 S t+1,8 V t,1 = 1 V t,2 = 1 V t,3 = 0 V t,4 = 1 V t,5 = 1 V t,6 = 1 V t,7 = 0 V t,8 = 1 S t,1 S t,2 S t,4 S t,5 S t,6 S t,8 V t+1,1 = 1 V t+1,2 = 1 V t+1,3 = 0 V t+1,4 = 1 V t+1,5 = 0 V t+1,6 = 1 V t+1,7 = 0 V t+1,8 = 1 S t+1,1 S t+1,2 S t+1,4 S t+1,6 S t+1,8 Wireless Network Server 13

14 Frame t D-Frame Extracted Selective Frame Propagation (SFP) U t,1 = 1 U t,2 = 1 U t,3 = 0 U t,4 = 1 U t,5 = 1 U t,6 = 1 U t,7 = 0 U t,8 = 1 R t,1 R t,2 R t,4 R t,5 R t,6 R t,8 Mobile Device Frame t+1 U t+1,1 = 1 U t+1,2 = 1 U t+1,3 = 1 U t+1,4 = 1 U t+1,5 = 0 U t+1,6 = 1 U t+1,7 = 0 U t+1,8 = 1 FP-Frame Extracted R t+1,1 R t+1,2 R t+1,3 R t+1,4 R t+1,6 R t+1,8 Interframe Codeword Similarity k k, 1 AND, r t t U U U k t, j t 1, j t 1, j j 1 j 1 Yes r k > t r? No SFP Encoding SCP Encoding V t+1,1 = 1 V t+1,2 = 1 V t+1,3 = 0 V t+1,4 = 1 V t+1,5 = 0 V t+1,6 = 1 V t+1,7 = 0 V t+1,8 = 1 S t+1,1 V t+1,1 = 1 V t+1,2 = 1 V t+1,3 = 0 V t+1,4 = 1 V t+1,5 = 0 V t+1,6 = 1 V t+1,7 = 0 V t+1,8 = 1 S t+1,1 S t+1,2 S t+1,2 S t+1,4 S t+1,5 S t+1,6 S t+1,8 S t+1,4 S t+1,6 S t+1,8 14

15 Number of inliers in geometric verification SFP + Local Search (SFP + LS) N geo ³ t geo Terminate query locally on mobile device Local Database N geo < t geo Send stream to server by SFP coding Remote Database Send local features and signatures for top ranked database candidates to mobile device Mobile Device Wireless Network Server 15

16 Embedded Global Signatures Codeword 1 Codeword 2 Codeword 3 Codeword 4 Codeword 5 Codeword 6 Codeword 7 Codeword 8 Level 1 Highest Bitrate Level 2 Medium Bitrate Level 3 Lowest Bitrate 16

17 Outline Related work on feature compression Interframe coding of global signatures Selective codeword propagation Selective frame propagation Selective frame propagation + local search Global signature embedding Coding and retrieval results 17

18 Analysis of Retrieval Performance 18

19 Stanford Streaming MAR Dataset Mobile Augmented Reality 32 VGA-resolution query videos recorded with a camera phone Database of 23 labeled objects + 1M distractor images [Makar et al., 2013] 19

20 Experimental Setup Interframe coding parameters N D-Frames = 1 and N FP-Frames = 29 for frame rate of 30 fps Interframe codeword similarity threshold: t r = 0.9 RANSAC threshold: t geo = 25 feature matches signature parameters 250 SIFT features extracted for every D-Frame Dimensionality reduction to d LDA = 32 Codebook of k = 190 codewords Retrieval accuracy vs. uplink bitrate comparison 20

21 Retrieval Accuracy vs. Uplink Bitrate 88x 24x 14x Embedding: Level 1 < 2 kbps Embedding: Level 2 Embedding: Level 3 21

22 Conclusions Developed efficient methods for interframe coding of a continuous stream of global signatures Adapts bitrate in response to current viewfinder contents Combines advantages of local and remote database search Performs local database update at same time as query expansion Analyzed how retrieval accuracy varies with bitrate Models distributions of correlation scores Quantifies bitrate savings for all coding methods Achieved substantial bitrate savings Reduces uplink bitrate by almost 2 orders of magnitude Requires only a small downlink bitrate 22

23 Thank You 23

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