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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

10. Image Segmentation

verfasst von : René Vidal, Yi Ma, S. Shankar Sastry

Erschienen in: Generalized Principal Component Analysis

Verlag: Springer New York

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Abstract

Image segmentation is the task of partitioning a natural image into multiple contiguous regions, also known as segments, whereby adjacent regions are separated by salient edges or contours, and each region consists of pixels with homogeneous color or texture. In computer vision, this is widely accepted as a crucial step for any high-level vision tasks such as object recognition and understanding image semantics.

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Vorheriges Kapitel Image Representation
Nächstes Kapitel Motion Segmentation
Fußnoten
1
Note that there is even ambiguity in segmentation done by different humans. In later sections, we will see how we could make such human-based evaluation somewhat meaningful.
 
2
We will see how to incorporate pairwise information such as edges into such a simplified framework later. In particular, as we will see, such information can be incorporated through a special initialization to the segmentation algorithm.
 
3
Another popular approach for constructing texture vectors is to use multivariate responses of a fixed 2D texture filter bank. A previous study by (Varma and Zisserman 2003) has argued that the difference in segmentation results between the two approaches is small, and yet it is more expensive to compute 2D filter bank responses.
 
4
The image segmentation example shown in Section 6.​4.​2 in Chapter 6 was done using such a coding length function.
 
5
For a large region with a sufficiently smooth boundary, the number of boundary-crossing windows is significantly smaller than the number of those in the interior. For boundary-crossing windows, their average coding length is roughly proportional to the number of pixels inside the region if the Gaussian distribution is sufficiently isotropic.
 
6
We use the publicly available code for this method available at http://​www.​cs.​sfu.​ca/​~mori/​research/​superpixels/​ with parameter N_sp = 200.
 
7
We will discuss several discrepancy measures in Section 10.4.2, such as the probabilistic Rand index (PRI) and variation of information (VOI).
 
8
The quantitative performance of several existing algorithms was also evaluated in a recent work ((Arbelaez et al. 2009)), which was published roughly at the same time as this work. The reported results therein generally agree with our findings.
 
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Metadaten
Titel
Image Segmentation
verfasst von
René Vidal
Yi Ma
S. Shankar Sastry
Copyright-Jahr
2016
Verlag
Springer New York
Buch
Generalized Principal Component Analysis

Print ISBN: 978-0-387-87810-2

Electronic ISBN: 978-0-387-87811-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-0-387-87811-9_10

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