Semantic contours from inverse detectors
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Semantic Contours from Inverse detectors. 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). Problem.

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Semantic Contours from Inverse detectors

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Semantic contours from inverse detectors

Semantic Contours from Inverse detectors


Outline

Outline

  • Introduction

  • Inverse detector

  • Localizing semantic contours

  • Experiments

  • Conclusion


Problem

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

Low-level contours

(No-class specific)

Problem

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

Class specific contours


Naive solution

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

Naive Solution

  • Using detector outputs will result is contours from surrounding context

  • To avoid this problem they propose the inverse detector


Outline1

Outline

  • Introduction

  • Inverse detector

  • Localizing semantic contours

  • Experiments

  • Conclusion


The inverse detector

  • Given localized contours I and object detector , the Inverse Detector produces the object contour image

The Inverse Detector

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)


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 RK, assigned into one of bins (from SO)

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

Feature Vector

  • 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

  • Inverse detectors is of the following form:

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

Inverse detectors

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


Outline2

Outline

  • Introduction

  • Inverse detector

  • Localizing semantic contours

  • Experiments

  • Conclusion


Localizing semantic contours using inverse detectors

  • System has two 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

  • Stage 1: train inverse detectors (of the following form) for each poselet (as discussed previously)

Localizing semantic contours using inverse detectors

  • 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


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

Combining information across categories

  • 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:


Outline3

Outline

  • Introduction

  • Inverse detector

  • Localizing semantic contours

  • Experiments

  • Conclusion


Experiments

Experiments


Semantic boundaries dataset sbd

  • PASCAL VOC2011, 20categories , 2223images

  • 8498 training images and 2820 test images

Semantic Boundaries Dataset (SBD)


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

Benchmark

precision and recall are practically zero


Experiments1

Experiments


Outline4

Outline

  • Introduction

  • Inverse detector

  • Localizing semantic contours

  • Experiments

  • Conclusion


Conclusion

Conclusion

  • Three distinct contributions

    • A new task

    • A new annotated dataset

    • A semantic contour detector


Thank you

Thank you 


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