Signal Processing - November 2017 - 40
(a)
Chair
Monitor
Table
Person
Car
(b)
FIGURE 1. (a) Example images and (b) their semantic segmentation
ground truths. Compared to image-level class labels and instance bounding boxes, the pixel-wise labels provide more dense and comprehensive
description of image content.
outperforming previous state of the art in many other computer
vision tasks such as human pose estimation [7], [53], face recognition [45], [48], and so on.
The great success of DCNNs also leads to another challenging visual recognition task called semantic segmentation.
The goal of semantic segmentation is to assign semantic class
labels to every pixel in images, where semantic classes typically include a diverse range of object categories (e.g., person,
dog, bus, bike) and background components (e.g., sky, road,
building, mountain). As illustrated in Figure 1, the result of
semantic segmentation is pixel-level masks of each semantic
class, which describe image content more comprehensively
than image-level class labels given by image classification and
object bounding boxes predicted by object detection.
Such a detailed image description is essential to build an intelligent system that is as competitive as human visual cognitive ability. Also, due to the emergence of computer vision applications
that require comprehensive understanding of visual input, such as
medical image analysis, autonomous driving, robotics, and human
computer interaction, the demand for accurate semantic segmentation algorithms has been increasing continually.
In return for its detailed high-level prediction capability,
however, semantic segmentation involves several critical challenges to be resolved. One has significant appearance variations of semantic classes caused by large intraclass variation,
occlusion, deformation, illumination change, and viewpoint
variation that are commonly observed in real-world images.
Being invariant to these factors is challenging especially for
semantic segmentation that has to predict class labels in a pixel
level. Also, semantic segmentation must consider structured
dependency among class labels of pixels during prediction (i.e.,
assigning the same class labels to spatially adjacent pixels), but
40
this constraint in semantic segmentation is difficult to handle
in practice due to a prohibitively large search space for possible
segmentation results.
Fortunately, DCNNs provide solutions to the aforementioned issues. The rich hierarchical feature representations of
DCNNs is robust against significant appearance variations.
Also, several architectures of DCNNs have been proposed to
predict structured output naturally by considering the structured dependency either implicitly [5], [32], [33] or explicitly
[27], [31], [57]. Furthermore, during their training, the feature
representation and the structured prediction of the networks
are jointly optimized in end-to-end manners. All of these factors are critical to overcome the previously mentioned difficulties in semantic segmentation. Consequently, DCNNs
have achieved substantial progress in semantic segmentation,
improving previous records based on handcrafted features significantly on public benchmarks including PASCAL Visual
Object Classes (VOC) [11].
Despite the great success of DCNNs on public benchmarks, there still remains a critical obstacle in the way of their
applications to semantic segmentation in an uncontrolled and
realistic environment: lack of annotated training images. It
has been known that, since a DCNN has a large number of
tunable parameters, it accordingly demands a large number
of annotated data for training models with good generalization performance. For semantic segmentation, however, collecting large-scale annotations is significantly labor intensive
because people have to manually draw pixel-level masks for
every semantic categories per image to carry out the annotation. Also, collecting annotations for semantic segmentation
is practically limited for some applications. An example is
medical image analysis, for which domain expert knowledge
is essential to accurate annotations. For these reasons, existing data sets often suffer from lack of annotated examples and
class diversity, and it is also difficult to maintain good quality of segmentation annotations in terms of both accuracy and
consistency. Therefore, it is not straightforward to extend the
existing models based on DCNNs to cover more classes while
maintaining high accuracy.
To resolve the issues related to training data collection
and make semantic segmentation more scalable and generally
applicable, researchers are interested in weakly supervised
learning. In this setting, the objective is to train a robust model
for semantic segmentation using the annotations that are much
weaker than pixel-wise labels. Examples of weak supervision
for semantic segmentation are illustrated in Figure 2. The clear
advantage of weak annotations is that they are much cheaper
to obtain than the standard segmentation annotations. Some
types of weak annotations such as image-level class labels and
bounding boxes are even readily available in existing largescale data sets [10], [29] for image classification and object
detection. Thus, with such weakly annotated images, we can
greatly enlarge or easily create training data sets for semantic
segmentation. The main issue of weakly supervised semantic segmentation is then how to fill the gap between the level
of supervision and that of prediction. The supervisory signal
IEEE SIGNAL PROCESSING MAGAZINE
|
November 2017
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Table of Contents for the Digital Edition of Signal Processing - November 2017
Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
Signal Processing - November 2017 - 4
Signal Processing - November 2017 - 5
Signal Processing - November 2017 - 6
Signal Processing - November 2017 - 7
Signal Processing - November 2017 - 8
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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