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P1151139820

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P1151139820

  1. 1. Improved Context-AwareImproved Context-Aware Saliency Detection UsingSaliency Detection Using Local Search WindowLocal Search Window Shanshan Wand and Amr Abdel-Dayem Department of Mathematics and Computer Science Laurentian University Ramsey Lake Road, Sudbury, Canada aabdeldayem@cs.laurentian.ca
  2. 2. ContentsContents • Introduction • Saliency Detection Approaches • Context-aware Saliency (CASal) • Improvement of CASal • Results • Conclusion 2International Conference on Computer Science and Engineering ICGST2011
  3. 3. IntroductionIntroduction • Problem definition Find an appropriate saliency map for content-aware image retargeting problem. oWhat is content-aware image retargeting (CAIR) problem? oWhat is saliency map? 3International Conference on Computer Science and Engineering ICGST2011
  4. 4. IntroductionIntroduction • Content-aware image retargeting (CAIR) o Given an image I with m×n and a new size m’×n’, produce a new image J with size m’×n’ , that will be a good representative of the image I. 4International Conference on Computer Science and Engineering ICGST2011
  5. 5. IntroductionIntroduction • Content-aware image retargeting (CAIR) o Given an image I with m×n and a new size m’×n’, produce a new image J with size m’×n’ , that will be a good representative of the image I. 5International Conference on Computer Science and Engineering ICGST2011
  6. 6. IntroductionIntroduction • Content-aware image retargeting (CAIR) o Given an image I with m×n and a new size m’×n’, produce a new image J with size m’×n’ , that will be a good representative of the image I. 6International Conference on Computer Science and Engineering ICGST2011
  7. 7. IntroductionIntroduction • Content-aware image retargeting (CAIR) o Given an image I with m×n and a new size m’×n’, produce a new image J with size m’×n’ , that will be a good representative of the image I. 7International Conference on Computer Science and Engineering ICGST2011
  8. 8. IntroductionIntroduction • Content-aware image retargeting (CAIR) o Given an image I with m×n and a new size m’×n’, produce a new image J with size m’×n’ , that will be a good representative of the image I. 8International Conference on Computer Science and Engineering ICGST2011
  9. 9. IntroductionIntroduction • Content-aware image retargeting (CAIR) o Given an image I with m×n and a new size m’×n’, produce a new image J with size m’×n’ , that will be a good representative of the image I. o The main objectives of retargeting methods: •Preserve the important content •Limit visual artifacts in the retargeted images •Preserve internal structures of the original images o CAIR is a kind of techniques, which retarget images considering important content. 9International Conference on Computer Science and Engineering ICGST2011
  10. 10. Saliency MapSaliency Map • Requirements of importance map in CAIR o Detect important objects and their surrounding contexts. o More important in the boundary of objects and internal structures, less important in the inner region of objects 10International Conference on Computer Science and Engineering ICGST2011
  11. 11. Saliency MapSaliency Map • Saliency map o Definition Saliency map is the output of saliency detection, which presents the sensory information before further processing. o How to determine saliency for an image region? The difference between it and its surrounding regions, based on various features . e.g. color, orientation, motion, high-level features (texts, faces), etc. o Applications •Content-aware image retargeting (CAIR) •Content-based information retrieval (CBIR) •Image browsing on mobile devices •Object recognition •Automatic cropping •Image summarization •… 11International Conference on Computer Science and Engineering ICGST2011
  12. 12. Saliency Detection ApproachesSaliency Detection Approaches o Low-level features • Intensity, color contrast, directions, etc. o High-Level features can be added • Edges, textures, text, faces,.....etc. • Common approaches o To identify the human vision attracted fixation points Itti’s method Graph-Based Visual Saliency (GBVS) o To detect a dominant object CRFSal o To detect dominant objects with their essential contexts Context-aware saliency detection (CASal) 12International Conference on Computer Science and Engineering ICGST2011
  13. 13. Saliency Detection ApproachesSaliency Detection Approaches original Itti’s method GBVS CRFSal CASal 13International Conference on Computer Science and Engineering ICGST2011
  14. 14. Context-aware Saliency (CASal)Context-aware Saliency (CASal) • Four Principles of Human Visual Attention o P1: Local low-level features Color contrast Saliency in the regions that have distinctive colors should be high o P2: Global features Suppress frequently occurring features o P3: Visual organization rules Visual forms consist of one or several centres of gravity o P4: High-level factors faces 14International Conference on Computer Science and Engineering ICGST2011
  15. 15. Context-aware Saliency (CASal)Context-aware Saliency (CASal) • Based On: o Dissimilarity Measure between Patch Pairs o Multiple-scale Saliency o Foci of Attention 15International Conference on Computer Science and Engineering ICGST2011
  16. 16. Context-aware Saliency (CASal)Context-aware Saliency (CASal) • Discussions o High accurate saliency map o Computationally too expensive Around 38 minutes (256 × 256) 16International Conference on Computer Science and Engineering ICGST2011
  17. 17. Improvement of CASalImprovement of CASal • Using single scale instead of multi scales • Using multi scales with patch size 7×7 • Adding local search window 17International Conference on Computer Science and Engineering ICGST2011
  18. 18. Improvement of CASalImprovement of CASal • Using single scale instead of multi scales Image scale 1/4r Patch size 3×3 Image scale 1/2r Patch size 5×5 Image scale r Patch size 7×7 Original image 18International Conference on Computer Science and Engineering ICGST2011
  19. 19. Improvement of CASalImprovement of CASal • Using multi scales with patch size 7×7 Multi scales Patch size 7×7 Original CASal Original image 19International Conference on Computer Science and Engineering ICGST2011
  20. 20. Improvement of CASalImprovement of CASal • Adding local search window Shrink the neighbouring region for searching the K most similar patches Original image Original CASal CASal with local window 57×57 CASal with local window 85×85 CASal with local window 113×113 20International Conference on Computer Science and Engineering ICGST2011
  21. 21. ResultsResults • 86 images from the benchmark for image retargeting • Three sizes for local windows are tested: 57×57 85×85 113×113 • Using local window with 85×85 produces almost the same saliency maps as the original CASal, but spends 21.7% of the time of the original CASal. Window size Average time (seconds) 57×57 267.82 85×85 498.15 113×113 824.18 Full image (original CASal [9]) 2294.67 Average running time using different search window sizes 21International Conference on Computer Science and Engineering ICGST2011
  22. 22. ResultsResults 22International Conference on Computer Science and Engineering ICGST2011
  23. 23. ResultsResults 23International Conference on Computer Science and Engineering ICGST2011
  24. 24. ResultsResults 24International Conference on Computer Science and Engineering ICGST2011
  25. 25. ResultsResults 25International Conference on Computer Science and Engineering ICGST2011
  26. 26. ConclusionConclusion • Using the local search window can reduce the computational cost of the CASal algorithm. • The proposed solution produces comparable saliency map to the original algorithm. • Finding simpler and more efficient similarity measure can further significantly reduce the algorithm’s running time in the future. 26International Conference on Computer Science and Engineering ICGST2011
  27. 27. Thank you 27International Conference on Computer Science and Engineering ICGST2011

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