Generating object segmentation proposals using global and local search

CVPR2014 POSTER

Outline

IntroductionMethodExperimentsConclusion

The sliding window technique suffers from the problem of high computational cost when the number of object categories is large.

we propose a fast method for producing object segmentation proposals by grouping superpixels.

Introduction

Introduction

1. Superpixels and feature extraction2. Refined superpixels3. Local search4. Global search

Method

Superpixels and feature extraction

We segment the input image into superpixels using two approaches.

The first approach is referred as SLIC and it produces relatively compact superpixels that have approximately equal size.

The Second approach is referred as FH and it produces very diverse set of superpixels that can be anything from half of the image to a narrow object boundary.

Superpixels and feature extraction

we use SIFT descriptors computed on a dense regular grid and RGB values extracted from each pixel.

Both descriptors are quantized using visual vocabulary that is learned using training data.

Refined superpixels

We first compute a similarity score for each pair of adjacent superpixels.

This score is defined for superpixel pair as

Local search

the search is split into several parallel branches.

This branch is referred as local search, since it considers only superpixel pairs when deciding the next proposal.

This approach fails to detect large non-homogeneous objects that consist of diverse set of superpixels.

Global search

define the general form of the energy function as

Experiments

1. Experiment 12. Experiment 23. Experiment with other datasets4. Comparison of execution times

Experiment 1

Experiment 2

Experiment with other datasets

Comparison of execution times

Experiments

Experiments

We have presented a fast approach for generating high-quality class-independent object segmentation proposals for color images.

Our experimental evaluation with annotated Pascal VOC images shows that the generated region proposals provide accurate segmentations for various kinds of objects.

Our approach is approximately as fast as the fastest available comparison method but provides substantially more accurate segmentations.

Conclusion