Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Provided Tags

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  • 1. Identifying Objects inImages from Analyzing theUser„s Gaze Movementsfor Provided TagsTina Walber, Ansgar Scherp, Steffen StaabUniversity of Koblenz-Landau, Koblenz, GermanyMultimedia Modeling ConferenceKlagenfurt, AustriaJanuary 4-6, 2012
  • 2. Motivation: Image Tagging tree girl car store people sidewalk  Find specific objects in images  Analyzing the user‟s gaze path only T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 2 of 21
  • 3. Research Questions1.Best fixation measure to find the correct image region given a specific tag?2. Can we differentiate two regions in the same image? T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 3 of 21
  • 4. 3 Steps Conducted by Users Look at red blinking dot Decide whether tag can be seen (“y” or “n”) T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 4 of 21
  • 5. Dataset LabelMe community images  Manually drawn polygons  Regions annotated with tags 182.657 images (August 2010) High-quality segmentation and annotation Used as ground truth T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 5 of 21
  • 6. Experiment Images and Tags Randomly selected 51 images Contain at least two tagged regions Created two tag sets for the 51 images Each image is assigned two tags (one per set) Tags are either “true” or “false”  “true”  object described by tag can be seen  “false”  object cannot be seen on the image Keep subjects concentrated during experiment T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 6 of 21
  • 7. Subjects & Experiment System 20 subjects  16 male, 4 female (age: 23-40, Ø=29.6)  Undergrads (6), PhD (12), office clerks (2) Experiment system  Simple web page in Internet Explorer  Standard notebook, resolution 1680x1050  Tobii X60 eye-tracker (60 Hz, 0.5° accuracy) T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 7 of 21
  • 8. Conducting the Experiment Each user looked at 51 tag-image-pairs First tag-image-pair dismissed 94.3% correct answers Equal for true/false tags ~3s until decision (average) 85% of users strongly agreed or agreed that they felt comfortable during the experiment  Eyetracker did not much influence comfort T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 8 of 21
  • 9. Pre-processing of Eye-tracking Data Obtained 547 gaze paths from 20 users where  Users gave correct answers  Image has “true” tag assigned Fixation extraction  Tobii Studio‟s velocity & distance thresholds  Fixation: focus on particular point on screen One fixation inside or near the correct region 476 (87%) gaze paths fulfill this requirement T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 9 of 21
  • 10. Analysis of Gaze Fixations (1) Applied 13 fixation measures on the 476 paths (2 new, 7 standard Tobii , 4 literature) Fixation measure: function on users‟ gaze paths Calculated for each image region, over all users viewing the same tag-image-pair T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 10 of 21
  • 11. Considered Fixation MeasuresNr Name Favorite region r Origin1 firstFixation No. of fixations before 1st on r Tobii2 secondFixation No. of fixations before 2nd on r [13]3 fixationsAfter No. of fixations after last on r [4]4 fixationsBeforeDecision fixationsAfter, but before decision New5 fixationsAfterDecision fixationsBeforeDecision and after New6 fixationDuration Total duration of all fixations on r Tobii7 firstFixationDuration Duration of first fixation on r Tobii8 lastFixationDuration Duration of last fixation on r [11]9 fixationCount Number of fixations on r Tobii10 maxVisitDuration Max time first fixation until outside r Tobii11 meanVisitDuration Mean time first fixation until outside r Tobii12 visitCount No. of fixations until outside r Tobii13 T. saccLength S. Staab – Identifying Objects in Imageslength, before fixation on r Walber, A. Scherp, Saccade [6]of 21 11
  • 12. Analysis of Gaze Fixations (2) For every image region (b) the fixation measure is calculated over all gaze paths (c) Results are summed up per region Regions ordered according to fixation measure If favorite region (d) and tag (a) match, result is true positive (tp), otherwise false positive (fp) T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 12 of 21
  • 13. Precision per Fixation Measure meanVisitDuration PSum of tp and fp assignments fixationsBeforeDecision lastFixationDuration fixationDuration Fixation measures T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 13 of 21
  • 14. Adding Boundaries and Weights Take eye-tracker inaccuracies into account Extension of region boundaries by 13 pixels Larger regions more likely to be fixated Give weight to regions < 5% of image size meanVisitDuration increases to P = 0.67 T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 14 of 21
  • 15. Examples: Tag-Region-Assignments T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 15 of 21
  • 16. Comparison with Baselines Naïve baseline: largest region r is favorite Random baseline: randomly select favorite r Gaze / Gaze* significantly better (χ², α<0.001) T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 16 of 21
  • 17. Effect of Gaze Path Aggregation P Number of gaze paths used Aggregation of precision P for Gaze* Single user still significantly better (χ² for naive with α<0.001 and random with α<0.002) T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 17 of 21
  • 18. Research Questions1.Best fixation measure to find the correct image region given a specific tag?  meanVisitDuration with precision of 67%2. Can we differentiate two regions in the same image? T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 18 of 21
  • 19. Differentiate Two Objects Use second tag set to identify different objects in the same image 16 images (of our 51) have two “true” tags 6 images had two correct regions identified  Proportion of 38% Average precision for single object is 67%  Correct tag assignment for two images: 44% T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 19 of 21
  • 20. Correctly Differentiated Objects T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 20 of 21
  • 21. Research Questions1.Best fixation measure to find the correct image region given a specific tag?  meanVisitDuration with precision of 67%2. Can we differentiate two regions in the same image?  Accuracy of 38%Acknowledgement: This research was partially supported by the EU projectsPetamedia (FP7-216444) andObjects in Images T. Walber, A. Scherp, S. Staab – Identifying SocialSensor (FP7-287975). 21 of 21
  • 22. Influence of Red Dot First 5 fixations, over all subjects and all images T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 22 of 21
  • 23. Experiment Data Cleaning Manually replaced images witha) Tags that are incomprehensible, require expert-knowledge, or nonsenseb) Tag refers to multiple regions, but not all are drawn into the image (e.g., bicycle)c) Obstructed objects (bicycle behind a car)d) “False”-tag actually refers to a visible part of the image and thus were “true” tags T. Walber, A. Scherp, S. Staab – Identifying Objects in Images 23 of 21