1 / 32

Large-Scale Nonparametric Image Parsing

sky. building. Large-Scale Nonparametric Image Parsing. car. road. Joseph Tighe and Svetlana Lazebnik University of North Carolina at Chapel Hill. CVPR 2011. Workshop on Large-Scale Learning for Vision. Small-scale image parsing Tens of classes, hundreds of images.

kemal
Download Presentation

Large-Scale Nonparametric Image Parsing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. sky building Large-Scale Nonparametric Image Parsing car road Joseph Tighe and Svetlana Lazebnik University of North Carolina at Chapel Hill CVPR 2011 Workshop on Large-Scale Learning for Vision

  2. Small-scale image parsingTens of classes, hundreds of images Figure from Shotton et al. (2009) He et al. (2004), Hoiem et al. (2005), Shotton et al. (2006, 2008, 2009), Verbeek and Triggs (2007), Rabinovich et al. (2007), Galleguillos et al. (2008), Gould et al. (2009), etc.

  3. Large-scale image parsingHundreds of classes, tens of thousands of images Non-uniform class frequencies

  4. Large-scale image parsingHundreds of classes, tens of thousands of images Non-uniform class frequencies Evolving training set http://labelme.csail.mit.edu/

  5. Challenges • What’s considered important for small-scale image parsing? • Combination of local cues • Multiple segmentations, multiple scales • Context • Graphical model inference (CRFs, etc.) • How much of this is feasible for large-scale, dynamic datasets?

  6. Our first attempt: A nonparametric approach • Lazy learning: do (almost) nothing at training time • At test time: • Find a retrieval set of similar images for each query image • Transfer labels from the retrieval set by matching segmentation regions (superpixels) • Related work: SIFT Flow (Liu et al. 2008, 2009)

  7. Step 1: Scene-level matching Gist (Oliva & Torralba, 2001) Spatial Pyramid(Lazebnik et al., 2006) Color Histogram Retrieval set: Source of possible labels Source of region-level matches

  8. Step 2: Region-level matching Superpixel features Superpixels(Felzenszwalb & Huttenlocher, 2004)

  9. Step 2: Region-level matching Road Tree Pixel Area (size) Sky Building Snow

  10. Step 2: Region-level matching Road Absolute mask(location) Sidewalk

  11. Step 2: Region-level matching Road Texture Snow Sky Sidewalk

  12. Step 2: Region-level matching Road Sidewalk Color histogram Building

  13. Region-level likelihoods • Nonparametric estimate of class-conditional densities for each class c and feature type k: • Per-feature likelihoods combined via Naïve Bayes: Features of class c within some radius of ri Total features of class c in the dataset kth feature type of ith region

  14. Region-level likelihoods Building Car Crosswalk Road Window Sky

  15. Step 3: Global image labeling • Compute a global image labeling by optimizing a Markov random field (MRF) energy function: Likelihood score for region ri and label ci Smoothing penalty Co-occurrence penalty Neighboring regions Regions Vector of region labels

  16. Step 3: Global image labeling • Compute a global image labeling by optimizing a Markov random field (MRF) energy function: Likelihood score for region ri and label ci Smoothing penalty Co-occurrence penalty Neighboring regions Regions Vector of region labels Maximum likelihood labeling Edge penalties Final labeling Final edge penalties sky sky building building window car car road road

  17. Step 3: Global image labeling • Compute a global image labeling by optimizing a Markov random field (MRF) energy function: Likelihood score for region ri and label ci Smoothing penalty Co-occurrence penalty Neighboring regions Regions Vector of region labels Maximum likelihood labeling Edge penalties Original image MRF labeling sky sky road tree sea sea road sand sand

  18. Joint geometric/semantic labeling • Semantic labels: road, grass, building, car, etc. • Geometric labels: sky, vertical, horizontal • Gould et al. (ICCV 2009) Original image Semantic labeling Geometric labeling sky sky tree vertical car horizontal road

  19. Joint geometric/semantic labeling • Objective function for joint labeling: Geometric labels Semantic labels Cost of geometric labeling Cost of semantic labeling Geometric/semantic consistency penalty Original image Semantic labeling Geometric labeling sky sky tree vertical car horizontal road

  20. Example of joint labeling

  21. Understanding scenes on many levels To appear at ICCV 2011

  22. Datasets

  23. Datasets

  24. Overall performance *SIFT Flow: 74.75

  25. Per-class classification rates

  26. Results on SIFT Flow dataset

  27. Results on LM+SUN dataset Image Ground truth Initial semantic Final semantic Final geometric 55.3 92.2 93.6

  28. Results on LM+SUN dataset Image Ground truth Initial semantic Final semantic Final geometric 58.9 57.3 93.0

  29. Results on LM+SUN dataset Image Ground truth Initial semantic Final semantic Final geometric 0.0 11.6 60.3 93.0

  30. Results on LM+SUN dataset Image Ground truth Initial semantic Final semantic Final geometric 75.8 87.7 65.6

  31. Running times Barcelona dataset SIFT Flow

  32. Conclusions • Lessons learned • Can go pretty far with very little learning • Good local features, global (scene) context more important than neighborhood context • What’s missing • A rich representation forscene understanding • The long tail • Scalable, dynamiclearning sky building car road

More Related