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Semantic Contours from Inverse detectorsPowerPoint Presentation

Semantic Contours from Inverse detectors

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## PowerPoint Slideshow about ' Semantic Contours from Inverse detectors' - dalton-franks

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Presentation Transcript

### Semantic Contours from Inverse detectors

### Problem

### Naive Solution

### The Inverse Detector

### Feature Vector

### Inverse detectors

### Localizing semantic contours using inverse detectors

### Combining information across categories

### Experiments contours in real world images

### Semantic Boundaries Dataset (SBD)

### Benchmark

### Thank you output, parameterized by the detection score

Outline

- Introduction
- Inverse detector
- Localizing semantic contours
- Experiments
- Conclusion

- Localizing and classifying category-specific object contours in real world images

Low-level contours

(No-class specific)

[ J.Malik ,IEEE Trans. on PAMI 2011 ]

Class specific contours

- 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

Outline contours in real world images

- Introduction
- Inverse detector
- Localizing semantic contours
- Experiments
- Conclusion

- Given localized contours contours in real world imagesI and object detector , the Inverse Detector produces the object contour image

Inverse detector

- I – image
- G – output of contour detector
- Gij – scores the likelihood of a pixel (i,j) lying on a contour
- R1, ..., Rl – l activation windows of the detector
- sk – score corresponding to each activation window Rk

- - Feature vector for pixel (i, j)

- Each detector window divided into contours in real world imagesS spatial bins
- Contours are binned into O orientation bins
- For a pixel (i, j), for an activation window RK, assigned into one of bins (from SO)

- Feature Vector at a location (i, j), and detector RK:

- en: an SO-dimensional vector with 1 in the nth position and 0 otherwise

- index of the bin into which the pixel (i, j) falls

- Feature vector for pixel (i, j):
- weighted sum of across all the activation windows

- Inverse detectors is of the following form: contours in real world images

- where, learn weight vector using a linear SVM with these features

Inverse detector

- Complete system: use of inverse detectors for localizing semantic contours
- Using poselet types object detectors[1]
- bottom-up contour detector[2]

[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

Outline contours in real world images

- Introduction
- Inverse detector
- Localizing semantic contours
- Experiments
- Conclusion

- System has contours in real world imagestwo stages
- traininverse detectors for each poselet types
- let Pposelets corresponding to category C be

- combine output of these inverse detectors to produce category-specific contours

- traininverse detectors for each poselet types

- 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

- Train a linear SVM (with classifying each pixel belonging to object contour or not)

- Features: concatenate the outputs of the inverse detectors corresponding to each of the poselet type

- Previous model: considers each category independently. contours in real world images
- 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:

Outline contours in real world images

- Introduction
- Inverse detector
- Localizing semantic contours
- Experiments
- Conclusion

- PASCAL VOC2011, 20categories , 2223images contours in real world images
- 8498 training images and 2820 test images

- 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

Experiments output, parameterized by the detection score

Outline output, parameterized by the detection score

- Introduction
- Inverse detector
- Localizing semantic contours
- Experiments
- Conclusion

Conclusion output, parameterized by the detection score

- Three distinct contributions
- A new task
- A new annotated dataset
- A semantic contour detector

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