1. Semantic Contours from Inverse Detectors Bharath Hariharan et.al. (ICCV-11)

2. Problem Localizing and classifying category-specific object contours in real world images Class specific contours Low-level contours (No-class specific)

3. Naive Solution Localizing and classifying category-specific object contours in real world images Using detector outputs will result is contours from surrounding context To avoid this problem they propose the inverse detector

4. - Feature vector for pixel (i, j) The Inverse Detector Given localized contours I and object detector, the Inverse Detector produces the object contour image I – image G – output of contour detector G ij – scores the likelihood of a pixel (i,j) lying on a contour R 1,..., R l – l activation windows of the detector s k – score corresponding to each activation window R k Inverse detector

5. Feature Vector Each detector window divided into S spatial bins Contours are binned into O orientation bins For a pixel (i, j), for an activation window R K, assigned into one of bins (from SO) Feature Vector at a location (i, j), and detector R K: index of the bin into which the pixel (i, j) falls e n : an SO-dimensional vector with 1 in the nth position and 0 otherwise Feature vector for pixel (i, j): weighted sum of across all the activation windows

6. Inverse detectors Inverse detectors is of the following form: Complete system: use of inverse detectors for localizing semantic contours Using poselet types object detectors[1] bottom-up contour detector[2] where, learn weight vector using a linear SVM with these features Inverse detector [1]-Detecting people using mutually consistent poselet activation. L. Bourdev et.al., ECCV-2010 [2] - Contour detection and hierarchical image segmentation. P. Arbelaez et.al, PAMI-2011

7. Localizing semantic contours using inverse detectors System has two stages train inverse detectors for each poselet types let P poselets corresponding to category C be combine output of these inverse detectors to produce category-specific contours Stage 1: train inverse detectors (of the following form) for each poselet (as discussed previously) Stage 2: combining the outputs of each of these inverse detectors Features: concatenate the outputs of the inverse detectors corresponding to each of the poselet type Train a linear SVM (with classifying each pixel belonging to object contour or not)

8. Combining information across categories Previous model: considers each category independently. In this model: combine information from across categories Propose two methods Method 1 First level: Train contour detector for each category separately Second level: Train on the outputs of these contour detectors Feature vector at the second level: Method 2 Only One level: Train on the features which are the outputs of the inverse detectors corresponding to the poselets of all categories Feature vector this level:

9. Semantic Boundaries Dataset (SBD) 8498 training images and 2820 test images (both instance specific and class specific)

10. Benchmark Show precision-recall curve for a detector producing soft output, parameterized by the detection score Report two summary statistics: Average precision (AP) maximal F-measure (MF) = (F = 2PR/(P+R) Precision: fraction of true contours among detections Recall: fraction of ground-truth contours detected precision and recall are practically zero

11. Experiments 8498 training images and 2820 test images Baseline comparison with the low level contour generated by contour detector[1] Improve both MF and AP by a factor of 5 wrt to the bottom up contour detector Single stage contour detector that combines the outputs of all inverse detectors across all categories does better than two stage detector. Best performance: transportation means (aeroplane, bicycle, bus, car, motorbike, train), people, bottles, TV monitors Worst: chairs, dining tables, potted plants, boats and birds (hard to detect) [1] - Contour detection and hierarchical image segmentation. P. Arbelaez et.al, PAMI-2011

12. Experiments

13. Thank you