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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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Presentation Transcript
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
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

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
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