Signal Processing - November 2017 - 43
one-bit information for each object class per image, which
model and, in turn, used to update the model as new groundindicates whether the size of the object occupies more than
truth annotations.
10% of the image area or not. This information is incorpoSince the supervision by image-level class labels is too
rated during training by enforcing that the output score map
coarse for segmentation, the quality of the obtained results
of the model satisfies the size constraint given by user.
is often not satisfactory in the above approaches. This issue
Objectness, which is also known as saliency, is another
has been tackled by incorporating additional cues to simuform of the prior knowledge that can provide information
late supervision for object location and shape. To incorporate
about object extents [4], [20], [34], [38]. It is a real-valued score
localization cue, techniques for discriminative localization
assigned to each pixel or local image region to indicate whethbased on DCNN are employed [58]. By carefully investigater the pixel or region belongs to an actual object, regardless
ing the contribution of each hidden units to the output class
of the semantic class. Since it is class agnostic, the objectness
score of the network, one can identify coarse locations of
typically covers a larger object area including nondiscriminadiscriminative parts of each class in an image. Then the outtive parts, thus is useful to compensate the limitation of weakly
puts from discriminative localization are used to choose seeds
supervised approaches that favor only small discriminative
indicating a position on the area of a semantic class, and the
parts. For this reason, Pinheiro and Colseeds are expanded to neighboring pixels to
lobert [38] adopt an off-the-shelf algorithm
estimate pixel-wise area of the class [22],
The main challenge in
that returns a set of region proposals with
[34], [46]. To incorporate shape informaweakly supervised
associated objectness scores [2]. The pertion, superpixels are utilized as units for
semantic segmentation
pixel objectness score is then computed
label assignment [24], [38]. A superpixel is
by aggregating the scores associated with
a group of neighboring pixels that are simithen is the incomplete
proposals and, in turn, is used to weight
lar in visual appearance (e.g., color) and is
annotations that miss
often obtained by clustering pixels based on
accurate object boundary the class score on corresponding pixel locations. In addition to image-level labels, the
low-level visual similarity. Superpixels are
information required to
objectness score has also been applied to
beneficial by encoding shape information,
learn segmentation.
different types of weak labels, such as the
as they naturally reflect a low-level image
point [4] and bounding box [20], to impose
structure such as object boundary. Pinheiro
larger weights on potential object areas. On the other hand,
and Collobert [38] employ superpixels to smooth pixel-wise
class labels within each superpixel as postprocessing. Kwak
Wei et al. [54] compute the saliency map on images associated
et al. [24] exploit superpixels as the layout of a pooling operawith a single class and use the obtained saliency masks to inition in the DCNN. Another popular approach to refining pixeltialize the network for pixel-wise classification. Similarly, Oh
level prediction is applying the fully connected CRF as in the
et al. [34] generate saliency masks using a model trained for
case of fully supervised approaches. CRF propagates labels
foreground segmentation, and they assign class labels on the
between neighboring pixels and refines the prediction from the
generated saliency mask by propagating the class label seeds
model to cover better object extent and shape.
obtained by the discriminative localization technique.
Although these approaches are able to roughly localize
objects, they often fail to infer accurate pixel-wise labels as they
Point supervision
tend to focus only on small discriminative parts (e.g., the head of
An instance-wise point, which roughly indicates the center locaan animal) instead of the whole body of an object. It is because
tion of an object, is the simplest form of weak annotations that
their objective during training is to minimize a classification loss,
provide object location information, since it can be obtained
which is easier to achieve by considering small parts that can be
by a single user click per object. Bearman et al. propose in [4]
well distinguished from other categories. Indeed, estimating
to employ a combined loss for both classification and localizapixel-wise labels only from image-wise labels is a significantly
tion, where the latter is used to ensure that a model predicts
ill-posed problem. To reduce the gap between coarse image-level
correct labels on the pixel localized by the point annotation.
labels and fine per-pixel labels, the approaches introduced in the
Since the point supervision is extremely sparse, in this work, an
next sections incorporate additional weak annotations together
additional prior knowledge on objectness is further employed to
with image-level class labels, utilize stronger but still weaker
-estimate foreground regions that well-cover the object.
annotations than pixel-level labels, or adopt additional data
sources that are also weakly annotated.
Bounding box
Although the point supervision provides coarse locations of
semantic classes, the information about areas covered by the
Prior knowledge
classes is still missing. A bounding box annotation can offer
One way to compensate for the lack of details in image-level
such information by indicating a rectangular area that tightly
class labels is to exploit extra prior knowledge about the segcovers the entire object region. Also, its annotation cost is
mentation target. Pathak et al. [36] proposed the employment
still cheaper than that of pixel-level segmentation annotation.
of prior knowledge about object size, which roughly provides
Existing approaches [9], [20], [35] that have been proinformation about how much area of an image is occupied by
posed to infer pixel-wise labels given bounding boxes
the target object. In this approach, a user is asked to provide
IEEE SIGNAL PROCESSING MAGAZINE
|
November 2017
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43
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
Signal Processing - November 2017 - 9
Signal Processing - November 2017 - 10
Signal Processing - November 2017 - 11
Signal Processing - November 2017 - 12
Signal Processing - November 2017 - 13
Signal Processing - November 2017 - 14
Signal Processing - November 2017 - 15
Signal Processing - November 2017 - 16
Signal Processing - November 2017 - 17
Signal Processing - November 2017 - 18
Signal Processing - November 2017 - 19
Signal Processing - November 2017 - 20
Signal Processing - November 2017 - 21
Signal Processing - November 2017 - 22
Signal Processing - November 2017 - 23
Signal Processing - November 2017 - 24
Signal Processing - November 2017 - 25
Signal Processing - November 2017 - 26
Signal Processing - November 2017 - 27
Signal Processing - November 2017 - 28
Signal Processing - November 2017 - 29
Signal Processing - November 2017 - 30
Signal Processing - November 2017 - 31
Signal Processing - November 2017 - 32
Signal Processing - November 2017 - 33
Signal Processing - November 2017 - 34
Signal Processing - November 2017 - 35
Signal Processing - November 2017 - 36
Signal Processing - November 2017 - 37
Signal Processing - November 2017 - 38
Signal Processing - November 2017 - 39
Signal Processing - November 2017 - 40
Signal Processing - November 2017 - 41
Signal Processing - November 2017 - 42
Signal Processing - November 2017 - 43
Signal Processing - November 2017 - 44
Signal Processing - November 2017 - 45
Signal Processing - November 2017 - 46
Signal Processing - November 2017 - 47
Signal Processing - November 2017 - 48
Signal Processing - November 2017 - 49
Signal Processing - November 2017 - 50
Signal Processing - November 2017 - 51
Signal Processing - November 2017 - 52
Signal Processing - November 2017 - 53
Signal Processing - November 2017 - 54
Signal Processing - November 2017 - 55
Signal Processing - November 2017 - 56
Signal Processing - November 2017 - 57
Signal Processing - November 2017 - 58
Signal Processing - November 2017 - 59
Signal Processing - November 2017 - 60
Signal Processing - November 2017 - 61
Signal Processing - November 2017 - 62
Signal Processing - November 2017 - 63
Signal Processing - November 2017 - 64
Signal Processing - November 2017 - 65
Signal Processing - November 2017 - 66
Signal Processing - November 2017 - 67
Signal Processing - November 2017 - 68
Signal Processing - November 2017 - 69
Signal Processing - November 2017 - 70
Signal Processing - November 2017 - 71
Signal Processing - November 2017 - 72
Signal Processing - November 2017 - 73
Signal Processing - November 2017 - 74
Signal Processing - November 2017 - 75
Signal Processing - November 2017 - 76
Signal Processing - November 2017 - 77
Signal Processing - November 2017 - 78
Signal Processing - November 2017 - 79
Signal Processing - November 2017 - 80
Signal Processing - November 2017 - 81
Signal Processing - November 2017 - 82
Signal Processing - November 2017 - 83
Signal Processing - November 2017 - 84
Signal Processing - November 2017 - 85
Signal Processing - November 2017 - 86
Signal Processing - November 2017 - 87
Signal Processing - November 2017 - 88
Signal Processing - November 2017 - 89
Signal Processing - November 2017 - 90
Signal Processing - November 2017 - 91
Signal Processing - November 2017 - 92
Signal Processing - November 2017 - 93
Signal Processing - November 2017 - 94
Signal Processing - November 2017 - 95
Signal Processing - November 2017 - 96
Signal Processing - November 2017 - 97
Signal Processing - November 2017 - 98
Signal Processing - November 2017 - 99
Signal Processing - November 2017 - 100
Signal Processing - November 2017 - 101
Signal Processing - November 2017 - 102
Signal Processing - November 2017 - 103
Signal Processing - November 2017 - 104
Signal Processing - November 2017 - 105
Signal Processing - November 2017 - 106
Signal Processing - November 2017 - 107
Signal Processing - November 2017 - 108
Signal Processing - November 2017 - 109
Signal Processing - November 2017 - 110
Signal Processing - November 2017 - 111
Signal Processing - November 2017 - 112
Signal Processing - November 2017 - 113
Signal Processing - November 2017 - 114
Signal Processing - November 2017 - 115
Signal Processing - November 2017 - 116
Signal Processing - November 2017 - 117
Signal Processing - November 2017 - 118
Signal Processing - November 2017 - 119
Signal Processing - November 2017 - 120
Signal Processing - November 2017 - 121
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Signal Processing - November 2017 - 123
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Signal Processing - November 2017 - 125
Signal Processing - November 2017 - 126
Signal Processing - November 2017 - 127
Signal Processing - November 2017 - 128
Signal Processing - November 2017 - 129
Signal Processing - November 2017 - 130
Signal Processing - November 2017 - 131
Signal Processing - November 2017 - 132
Signal Processing - November 2017 - 133
Signal Processing - November 2017 - 134
Signal Processing - November 2017 - 135
Signal Processing - November 2017 - 136
Signal Processing - November 2017 - 137
Signal Processing - November 2017 - 138
Signal Processing - November 2017 - 139
Signal Processing - November 2017 - 140
Signal Processing - November 2017 - 141
Signal Processing - November 2017 - 142
Signal Processing - November 2017 - 143
Signal Processing - November 2017 - 144
Signal Processing - November 2017 - 145
Signal Processing - November 2017 - 146
Signal Processing - November 2017 - 147
Signal Processing - November 2017 - 148
Signal Processing - November 2017 - 149
Signal Processing - November 2017 - 150
Signal Processing - November 2017 - 151
Signal Processing - November 2017 - 152
Signal Processing - November 2017 - 153
Signal Processing - November 2017 - 154
Signal Processing - November 2017 - 155
Signal Processing - November 2017 - 156
Signal Processing - November 2017 - 157
Signal Processing - November 2017 - 158
Signal Processing - November 2017 - 159
Signal Processing - November 2017 - 160
Signal Processing - November 2017 - 161
Signal Processing - November 2017 - 162
Signal Processing - November 2017 - 163
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Signal Processing - November 2017 - 165
Signal Processing - November 2017 - 166
Signal Processing - November 2017 - 167
Signal Processing - November 2017 - 168
Signal Processing - November 2017 - 169
Signal Processing - November 2017 - 170
Signal Processing - November 2017 - 171
Signal Processing - November 2017 - 172
Signal Processing - November 2017 - 173
Signal Processing - November 2017 - 174
Signal Processing - November 2017 - 175
Signal Processing - November 2017 - 176
Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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