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(Reading Group) Saliency Detection: A Boolean Map Approach
The document presents a novel boolean map approach for saliency detection, emphasizing the identification of significant image areas through binary representations based on color channel thresholds. It details methodologies and experiments that demonstrate the effectiveness of this approach in applications like image segmentation and object recognition. The findings indicate that this method achieves state-of-the-art performance in predicting eye fixation and suggests future enhancements by incorporating additional feature channels.
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(Reading Group) Saliency Detection: A Boolean Map Approach
- 1. Saliency Detection: ABoolean Map Approach IEEE International Conference on Computer Vision (ICCV), 2013 Jianming Zhang , Stan Sclaroff Department of Computer Science, Boston University 8/18/2014 1B31XM Advanced Image Analysis Team Members : H.Kidane, I.Sadek, M.Elawady Heriot Watt University School of Electrical and Physical Sciences
- 2. Outline • Introduction • Relatedwork • Methodology • Experiments • Conclusion • Future Work 8/18/2014 B31XM Advanced Image Analysis 2
- 3. Outline • Introduction • Relatedwork • Methodology • Experiments • Conclusion • Future Work 8/18/2014 B31XM Advanced Image Analysis 3
- 4. • Goal – Proposinga Boolean map based saliency . An image is represented in a set of binary images by randomly thresholding the image’s color channel. based on figure ground segregation • What is saliency ! – Saliency at a given location = how different this location is from its surround color, orientation, motion, depth etc (Koch an Ullman, 1985, Itti et al. 1998). This is usually called (Bottom up saliency) • Applications – Image segmentation – Object recognition – Visual tracking 8/18/2014 B31XM Advanced Image Analysis 4 Introduction
- 5. 8/18/2014 B31XM AdvancedImage Analysis 5 Introduction Feature Search Fast & Effortless Conjunction Search Slow& Effortful VisualInput SaliencyMap Visual Saliency Map
- 6. Reaction time vs.number of distractors 8/18/2014 B31XM Advanced Image Analysis 6 Introduction Feature Search Conjunction Search Reactiontimetofindthe target # of distractors (Koch & Ullman 1985, Wolfe et al 1989, Itti & Koch 2000)
- 7. • Figure groundsegregation: – It is known as identifying the figure from the background • This image can be perceived as: – a vase shape in front of a black background – two black faces on a white background 8/18/2014 B31XM Advanced Image Analysis 7 Introduction Rubin's Vase
- 8. Outlines • Introduction • Relatedwork • Methodology • Experiments • Conclusion • Future Work 8/18/2014 B31XM Advanced Image Analysis 8
- 9. 8/18/2014 B31XM AdvancedImage Analysis 9 Related Work Method Limitation Center surround difference Cannot detect large salient region efficiently Scale variant The negative logarithm of probability Hierarchical decomposition Spectral domain analysis Machine learning Methods based on topological structure information Strong influence on visual attention Scale invariant
- 10. Outline • Introduction • Relatedwork • Methodology • Experiments • Conclusion • Future Work 8/18/2014 B31XM Advanced Image Analysis 10
- 11. 8/18/2014 B31XM AdvancedImage Analysis 11 Methodology Input Image Boolean Maps Attention Maps Saliency Mean of Attention Maps
- 12. It is generatedby randomly thresholding an input image I Where donates feature map of I Randomly generated threshold in the range [0, 255] CIE lab color space (perceptual uniformity) 8/18/2014 B31XM Advanced Image Analysis 12 Boolean MAP )),(( ITHRESHBi )(I Methodology
- 13. • the attentionmap A(B) is computed based on Gestalt Principle for figure-ground segregation from B • Gestalt Principle: surrounded regions are more likely to be perceived as figure 8/18/2014 B31XM Advanced Image Analysis 13 Attention Map Methodology
- 14. • Given aBoolean map B, and attention Map A(B), the saliency is modeled by the mean attention map given by 8/18/2014 B31XM Advanced Image Analysis 14 A dBIBpBAA )/()( Methodology
- 15. 8/18/2014 B31XM AdvancedImage Analysis 15 Methodology
- 16. Outline • Introduction • Relatedwork • Methodology • Experiments • Conclusion • Future Work 8/18/2014 B31XM Advanced Image Analysis 16
- 17. 8/18/2014 B31XM AdvancedImage Analysis 17 Experiments
- 18. Experiments Eye Fixation Prediction Implementation details Widthof image resizing 600 Width of kernel for opening operation 5 Sampling step size 8 Width of kernel for dilation operation 7 Width of kernel for opening operation (before Gaussian blurring) 23 Standard deviation of Gaussian blurring 20 Removing Small peaks on mean attention map http://mentormate.com/ 8/18/2014 18B31XM Advanced Image Analysis
- 19. Datasets MIT 1003 Toronto 120 Kootstra100 Cerf 181 ImgSal 235 http://www.cse.cuhk.edu.hk Evaluation Metric AUC Shuffled AUC Border CutCenter-Bias Sampling Experiments Eye Fixation Prediction 8/18/2014 19B31XM Advanced Image Analysis
- 20. Original GT BMS ΔMQDCT SigSal LG AWS HFT CAS Judd AIMGBVS Itti Experiments Eye FixationPrediction 8/18/2014 20B31XM Advanced Image Analysis
- 21. Original GT BMS ΔMQDCT SigSal LG AWS HFT CAS Judd AIMGBVS Itti Experiments Eye FixationPrediction 8/18/2014 21B31XM Advanced Image Analysis
- 22. Original GT BMS ΔMQDCT SigSal LG AWS HFT CAS Judd AIMGBVS Itti Experiments Eye FixationPrediction 8/18/2014 22B31XM Advanced Image Analysis
- 23. Experiments Eye Fixation Prediction 8/18/201423B31XM Advanced Image Analysis
- 24. Experiments Eye Fixation Prediction 8/18/201424B31XM Advanced Image Analysis
- 25. Experiments Eye Fixation Prediction 8/18/201425B31XM Advanced Image Analysis
- 26. Experiments Eye Fixation Prediction 8/18/201426B31XM Advanced Image Analysis
- 27. Experiments Eye Fixation Prediction 8/18/201427B31XM Advanced Image Analysis
- 28. Optimal average shuffled-AUC with correspondingGaussian blur STD 2012 2012 2012 2011 2013 2012 2009 2009 2007 1998 Less Background Distraction No Multi-Scale Processing Experiments Eye Fixation Prediction 8/18/2014 28B31XM Advanced Image Analysis
- 29. Runtime performance Programming Language C++ Image Size 600x400 Processor2.5 GHz Running OS Windows Memory 2 GB http://runtime.bordeaux.inria.fr CAS 78 LG 13 AWS 10 Judd 6.5 AIM 4.8 GBVS 1.1 ΔQDCT 0.49 Itti 0.43 HFT 0.27 SigSal 0.12 BMS 0.38 Experiments Eye Fixation Prediction 8/18/2014 29B31XM Advanced Image Analysis
- 30. Experiments Salient Object Detection High Blurred Binary Image Widthof kernel for opening operation for boolean maps is modified to 13 Turning off the dilation operation for attention maps Post-processing for mean attention map using (opening, closing) operations with kernel size (5) Binarizing saliency map at a fixed threshold Object Level Segmentation 8/18/2014 30B31XM Advanced Image Analysis
- 31. Original GT BMS GSSP HSal RC FT CAS HFT Experiments Salient Object Detection 8/18/201431B31XM Advanced Image Analysis
- 32. Experiments Salient Object Detection ASD Dataset 8/18/201432B31XM Advanced Image Analysis
- 33. Experiments Salient Object Detection ASD Dataset 8/18/201433B31XM Advanced Image Analysis
- 34. Outlines • Introduction • Relatedwork • Methodology • Experiments • Conclusion • Future Work 8/18/2014 B31XM Advanced Image Analysis 34
- 35. Conclusion •Helpful in figure-groundsegregation BMS has powerful advantage in surroundence aspect •Best results in different five eye- tracking datasets •Proper results in salient object detection BMS is only model that consistently achieves the state-of- art performance 8/18/2014 35B31XM Advanced Image Analysis
- 36. Outlines • Introduction • Relatedwork • Methodology • Experiments • Conclusion • Future Work 8/18/2014 B31XM Advanced Image Analysis 36
- 37. Future Work • Notonly color channels • Feature channels (i.e. orientation, depth, and motion) Improve the effectiveness of BMS • Integrating more saliency cues (i.e. convexity, symmetry, and familiarity) instead of current one (eliminating regions that touch image borders) Improve the attention map computation 8/18/2014 37B31XM Advanced Image Analysis
- 38. 8/18/2014 B31XM AdvancedImage Analysis 38