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Pablo Arbel´aez 1 , Bharath Hariharan 1 , Chunhui Gu 1,2 , Saurabh Gupta 1 , Lubomir Bourdev 1,3 ,† and Jitendra Malik 1 1 University of California, Berkeley - Berkeley, CA 94720 2 Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA 94043 - PowerPoint PPT Presentation
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SEMANTIC SEGMENTATION USING REGIONS AND PARTS
Pablo Arbel´aez1, Bharath Hariharan1, Chunhui Gu1,2, Saurabh Gupta1, Lubomir Bourdev1,3,† and Jitendra Malik1
1University of California, Berkeley - Berkeley, CA 947202Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA 940433Facebook, 1601 Willow Rd, Menlo Park, CA 94025
OUTLINE
Introduction Related Work Region Generation Region Representation Region Scoring Pixel Classification Experiments
INTRODUCTION
Bottom-up region cues and top-down part detectors provide complementary information for recognizing articulated objects.
INTRODUCTION
RELATED WORK
CRF Approaches Refining top-down detections Scoring bottom-up region
hypotheses
REGION GENERATION
Uses bottom-up regions as object candidates
Generate object candidates building on the segmentation method of [4]
Compute UCMs at three resolutions of the input image
[4] P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchical image segmentation. IEEE Trans. on PAMI, 2011.
REGION REPRESENTATION
Part Compatibility Features Part Activations
use the poselet framework introduced in [8, 7] use pre-trained models and masks from [9]
Part-Based Region Ranking
|I | : the total area of the imageα = (α1, ..., α6) ∈ N6
[7] L. Bourdev, S. Maji, T. Brox, and J. Malik. Detecting people using mutually consistent poselet activations. In Proc. ECCV, 2010.[8] L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3d human pose annotations. In Proc. ICCV, 2009.[9] T. Brox, L. Bourdev, S. Maji, and J. Malik. Object segmentation by alignment of poselet activations to image contours. In Proc. CVPR, 2011
REGION REPRESENTATION
Part Compatibility Features Part-Based Region Ranking
P = {P1, ..., PA}
REGION REPRESENTATION
Global Appearance Features a set of first-order appearance cues defined
on the region support Shape, Color, Texture
Semantic Contours Features 4 region features per semantic contour map
Generic geometrical properties 16 generic geometric properties for each
region
REGION REPRESENTATION
Multi-Class Features the three high-level descriptor types are
category-specific and the low-level geometric properties are shared
REGION SCORING
Predict the probability of belonging to each category of interest for each object candidate
After classification, each region is assigned a score for all the categories of interest
PIXEL CLASSIFICATION
Train a final set of classifiers that operate on pixels rather than on regions average maximum non-max suppression
EXPERIMENTS
Control Experiments
Calibration of multiple detectors through pixel classification
EXPERIMENTS
Test set performance
EXPERIMENTS