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Mixed-Resolution Patch-Matching

Mixed-Resolution Patch-Matching. Harshit Sureka Guide: Dr. P. J. Narayanan. Patch-Matching. 01/08/13. 2. Scope of Patch-Matching Algorithms. Image credits: Image Montage (Simakov et al. 2008). Object Detection,

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Mixed-Resolution Patch-Matching

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  1. Mixed-Resolution Patch-Matching Harshit Sureka Guide: Dr. P. J. Narayanan

  2. Patch-Matching 01/08/13 2

  3. Scope of Patch-Matching Algorithms Image credits: Image Montage (Simakov et al. 2008). Object Detection, Forgery Detection and Image Reshuffling (Barnes et al. 2010). Texture synthesis and Image completion (Xiao et al. 2011) 01/08/13 3

  4. Brute force v/s state-of-the-art Image Credit: Kumar et al. (ECCV 2008) • Find the matching patches to the patch in red • Brute-force: 2125 msec • State-of-the-art approaches: 1.5msec (1400x faster) 01/08/13 4

  5. The Patch-Matching Problem Image Credit: Barnes et al. (SIGGRAPH 2009) 01/08/13 5

  6. Types of Patch-Matching • Sparse • Finding matches for a few key-points • Dense • Matches for all patches in the source Image • Local • Search for matches in a specific region in the target image • Global • Search everywhere for matches in the target image • Local is computationally less expensive • But, will it suffice? Sparse Dense Global Local 01/08/13 6

  7. Where do similar patches lie? • Our focus dense and global • Since a lot of patches lie at large distances 01/08/13 7

  8. Till now.. • What is a patch? • The formal patch-matching problem • Motivation for making patch-matching algorithms faster • Types of patch-matching Next.. • Parameters of Patch-matching algorithms • Related Work 01/08/13 8

  9. Parameters of Patch-Matching Algorithms 01/08/13 9

  10. Previous Efforts • To use general-purpose Approximate Nearest Neighbor (ANN) algorithms • KD trees • PCA trees • etc. • They provide good solutions to general ANN problems • Do not take advantage of the structure and properties specific to images 01/08/13 10

  11. Previous Efforts: PatchMatch • Barnes et al., SIGGRAPH 2009 • Randomized cooperative Hill-Climbing Search • Based on the property of coherence in image patches • 3 steps of the algorithm: • Up-to 2x faster than all previous approaches • Enables several interactive applications • Not as accurate as one would hope • Coherency Assumption: Not true in highly textured images 01/08/13 11 Image credit: Barnes et al. 2009

  12. Previous state-of-the-art: Coherency Sensitive Hashing (CSH) • Korman and Avidan, ICCV 2011 • Based on PatchMatch • Replaces the Random step of PatchMatch with a Hash Table • Combines cues of location (of coherence) and appearance in a novel manner Coherence Appearance Type 3 Type 2 Type 1 01/08/13 12

  13. Coherency Sensitive Hashing (CSH) • Targeted propagation of information • Lower RMS error values in early iterations compared to PatchMatch • Faster and more accurate than Patchmatch • Less dependent on the coherency assumption • Lowest reconstruction errors • Current state-of-the-art 01/08/13 13

  14. Till now.. • Introduction to Patch-Matching • Previous techniques • General purpose ANN • PatchMatch • Coherency Sensitive Hashing (CSH) Next.. • Image Pyramids • Pyramids for Patch-Matching 01/08/13 14

  15. Pyramids • Image represented at multiple scales 512x512 32x32 Level 4 Level 0 Level 3 Level 2 Level 1 01/08/13 15

  16. Pyramids • Pyramid coarse-to-fine framework is well established (Burt et al. 1984) • Help a variety of problems • Image compression • Image alignment • Optical flow • Object tracking • Matching: do away with fine details, look at only high level information Can they also be used for Patch-Matching? Why not! 01/08/13 16

  17. Pyramid Patch-Matching • Novel patch-matching algorithm • Pyramid representation of original images • Coarsest level image size determined by a threshold ‘Pthold’ • Exhaustive global search at small images of coarsest level • Search localization at finer levels by up-sampling the matches found at coarser levels 01/08/13 17

  18. Pyramid Patch-Matching (PPM) • Coarsest level • Finer levels 01/08/13 18 a* is the patch in IA(p+1) corresponding to a

  19. PPM: Real Image example 01/08/13 19

  20. Pyramid Patch Matching • Increase K • Best match at coarsest resolution may not be the best match at original resolution • Store more number of matches • Wider search range at finer levels • Up to 2% better accuracy • Reduce Patch-Size • Small size patches matched at coarser resolutions • Early Termination • Of distance calculation if it exceeds the distance of the farthest patch currently in the matched list of patches • Up to 1.7x speed-up 01/08/13 20

  21. Parallel Implementation • PPM processes each patch independently • Pyramid construction • Standard parallel implementation • Coarsest level • Each thread assigned a block of source patches • Searches for best matches in the whole target image • Finer levels • Up-sampling of matches; candidate-list generation • Each thread searches the candidate space of the patches in its block • Near-linear time speed up w.r.t. number of cores 01/08/13 21

  22. GPU Implementation • Pyramid construction on the GPU • Standard implementation • Each patch is matched independently • Distance calculation of each query patch with its candidate matches is also independent 01/08/13 22

  23. GPU Implementation • Algorithm • Exhaustive search at the coarsest image resolution • Localized search at finer resolution levels • For each patch • threads equal to the number of candidate patches are invoked • Each thread handles one vector-distance calculation • Up-sampling of matches; candidate-list generation also in parallel • Additional 70x compared to implementation run on 4-core (~250x compared to single-core) 01/08/13 23

  24. GPU Timings • Values for CSH Dataset Images (shown in next slide) • Brute force for Lena (256x256 image) • Most gain in speed achieved for brute-force global exhaustive search 01/08/13 24

  25. CSH Dataset for Experiments • 133 image-pairs from the dataset provided by Korman and Avidan, ICCV 2011 (CSH) • Both images of the same scene of a movie • between 1 and 30 frames apart in the video 01/08/13 25

  26. Parameters Error: RMS distance between RGB values of patches, averaged over all matches of a patch and over all patches in the image Platform: Intel i7 920 processor, 2.67 GHz Varying KNN and Patch-size Varying Search-range Varying Pthold Values over all 133 pairs of images in the CSH dataset (shown in next slide) 01/08/13 26

  27. Experiment I • Proximity to ground-truth • Brute-force search for ground-truth matches • PPM captures up to 4% more ground truth matches • Better performance for more number of matches required 01/08/13 27

  28. Experiment II • Error (RMS error between RGB values of patches and their matches) • Parameters set to achieve similar run-times • PPM achieves consistently lower error values than CSH • Better performance than state-of-the-art • Much closer to ground truth (Calculated by Brute-force on GPU) 01/08/13 28

  29. Till now.. • Patch-Matching and previous techniques • Pyramid Patch-Matching • Improvements: Increase K, Reduce patch-size, early-termination • Experiments: State-of-the-art performance Next.. • Mixed-Resolution Vectors (MR-vectors) • Motivation for mixing resolutions • Mixed-Resolution Patch-Matching (MRPM) 01/08/13 29

  30. Mixed-Resolution: Motivation • Images at coarser resolutions contain approximate information • Small objects may get smoothed out due to downsampling • A match missed at the coarsest resolution will never be recovered later in the algorithm • Solution: More information already exists at finer resolutions. Use it! 01/08/13 30

  31. Mixed-Resolution Vectors • Idea • Concatenate corresponding patch-vectors of finer resolutions and current resolution • Including Information • Currently we pick a same-sized patch around the center of the corresponding up-sampled patch • Available prior knowledge about the image or patches can be used to pick information more wisely! 01/08/13 31

  32. MR-Vectors • Primary contribution • Can include information from multiple levels 01/08/13 32

  33. Mixed-Resolution Patch-Matching • In the framework of PPM, we replace traditional patch-vectors with MR-vectors • Rest of the algorithm remains same • We call this the “Mixed-Resolution Patch-Matching” Algorithm (MRPM). 01/08/13 33

  34. Comparison with increasing search range • Accuracy increases with cost of time when using a wider search range around up-sampled matches • Similar behavior is observed while mixing resolutions Mixed-Resolution achieves lower error values with less cost of time 01/08/13 34

  35. Parallel Implementation • Fast and efficient parallel implementations of MRPM follow from parallel implementations of PPM • Multicore: Near-linear time speed-up • GPU: All levels already on GPU memory 01/08/13 35

  36. PPM v/s MRPM (l = 2) • Lower error values with marginal cost of time • Less effect of time cost on GPU performance • Note: error not averaged over patch-size 01/08/13 36

  37. Experiment I • Proximity to ground truth • Up to 8% more of the ground truth matches are captured by MRPM than CSH • 4% more than PPM 01/08/13 37

  38. Experiment II • Error • Parameters set to achieve similar run time • MRPM achieves near optimal patch-matching 01/08/13 38

  39. Till now.. • Patch-Matching and previous techniques • Pyramid Patch-Matching • Mixed-Resolution Vectors • Mixed-Resolution Patch-Matching Next.. • Applications • Conclusion 01/08/13 39

  40. Applications • A variety of applications have a patch-matching engine at their core • MRPM can act as that engine and help improve quality and speed of several of these applications • We discuss 3 of these • Image de-noising • Image summarization (Retargeting) • Auto-crop 01/08/13 40

  41. Image denoising • Performs a weighted averaging over similar patches to de-noise the image (Non-local means, Buades et al., 2005) • Currently local due to computational constraints • Also improves accuracy in some cases • MRPM can be used to extend it to global 01/08/13 41

  42. Dataset of 36 images Experiments • Dataset of 36 images collected for evaluating denoising performance Combining matches of local (non-local means) and global (MRPM) 01/08/13 42

  43. Self-similar Image • Evaluation: Method-noise • Less structure removed from Image PSNR: 33.35 PSNR: 34.03 01/08/13 43

  44. Image Retargeting (Summarization) • Simakov et al. 2008, Bidirectional Similarity 01/08/13 44

  45. Results • MRPM achieves better visual qualityof results 01/08/13 45

  46. Auto-crop • Similar to Bi-directional Similarity • Coherence term muted: since all cropping windows would anyway be coherent with the image • Cropping based on completeness term similarity 01/08/13 46

  47. More Results 01/08/13 47

  48. Limitation: Image Reconstruction • Given an Image B and a dense patch-matching from A to B. Reconstruct image A • Each pixel is replaces with the average of the corresponding pixels that it is mapped to by all patches that contain it 01/08/13 48

  49. Summary • Presented a Pyramid based approach to the problem of patch-matching • 3 Improvements to improve accuracy and efficiency • Achieved state-of-the-art matching accuracy • Introduced the concept of Mixed-Resolution Vectors • Proposed Mixed-Resolution Patch-Matching outperforming state-of-the-art by up-to 8% more ground-truth matches captured 01/08/13 49

  50. Future work • Include coherency cues in the framework of the algorithm • Up-sampling maintains some coherency • Matching across all rotations and scale • Matching in videos • Explore the scope of MR-vectors • We believe it is a generic concept and the idea can be used in several other problems that use the multi-resolution technique 01/08/13 50

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