09-02-2013, 10:21 AM
Outdoor Scene Image Segmentation Based on Background Recognition and Perceptual Organization
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Abstract
In this paper, we propose a novel outdoor scene image
segmentation algorithm based on background recognition and
perceptual organization. We recognize the background objects
such as the sky, the ground, and vegetation based on the color
and texture information. For the structurally challenging objects,
which usually consist of multiple constituent parts, we developed
a perceptual organization model that can capture the nonaccidental
structural relationships among the constituent parts of the
structured objects and, hence, group them together accordingly
without depending on a priori knowledge of the specific objects.
Our experimental results show that our proposed method outperformed
two state-of-the-art image segmentation approaches on
two challenging outdoor databases (Gould data set and Berkeley
segmentation data set) and achieved accurate segmentation
quality on various outdoor natural scene environments.
INTRODUCTION
I MAGE segmentation is considered to be one of the fundamental
problems for computer vision. A primary goal of
image segmentation is to partition an image into regions of coherent
properties so that each region corresponds to an object or
area of interest [30]. In general, objects in outdoor scenes can be
divided into two categories, namely, unstructured objects (e.g.,
sky, roads, trees, grass, etc.) and structured objects (e.g., cars,
buildings, people, etc.). Unstructured objects usually comprise
the backgrounds of images. The background objects usually
have nearly homogenous surfaces and are distinct from the
structured objects in images. Many recent appearance-based
methods have achieved high accuracy in recognizing these
background object classes [40], [41], [53].
RELATED WORK
Bottom-up image segmentation methods only utilize
low-level features such as colors, textures, and edges to
decompose an image into uniform regions. Bottom-up methods
can be divided into two categories, namely, region-based
and contour-based approaches. A group of approaches treats
image segmentation as a graph cut problem. Shi and Malik
[6] proposed the normalized cut criterion that removes the
trivial solutions of cutting small sets of isolated nodes in the
graph. Felzenszwalb and Huttenlocher [5] proposed an efficient
graph-based generic image segmentation algorithm. As with the
normalized cut method, this method also tries to capture nonlocal
image characteristics. Comaniciu and Meer [48] treated
image segmentation as a cluster problem in a spatial-range
feature space. Their mean-shift segmentation algorithm has
illustrated excellent performance on different image data sets
and has been considered as one of the best bottom-up image
segmentation methods. Some of these region-based methods
have been widely used to generate coherent regions called
superpixels for many applications [1], [42], [51], [53].
IMAGE SEGMENTATION ALGORITHM
Here, we present a novel image segmentation algorithm for
outdoor scenes. Our research objective here is to explore detecting
object boundaries solely based on some general properties
of the real-world objects, such as perceptual organization
laws, without depending on object-specific knowledge. Our
image segmentation algorithm is inspired by a POM, which is
the main contribution of this paper. The POM quantitatively incorporates
a list of Gestalt cues. By doing this, the POM can
detect many structured object boundaries without having any
object-specific knowledge of these objects.
POM
Most images consist of background and foreground objects.
Most foreground objects are structured objects that are often
composed of multiple parts, with each part having distinct surface
characteristics (e.g., color, texture, etc.). Assume that we
can use a bottom-up method to segment an image into uniform
patches, then most structured objects should be oversegmented
to multiple patches (parts). After the background patches are
identified in the image, the majority of the remaining image
patches correspond to the constituent parts of structured objects
[see Fig. 4(b)] for an example). The challenge here is how
to piece the set of constituted parts of a structured object together
to form a region that corresponds to the structured object
without any object-specific knowledge of the object.
Image Segmentation Algorithm
The POM introduced in Section III-B can capture the special
structural relationships that obey the principle of nonaccidentalness
among the constituent parts of a structured object. To
apply the proposed POM to real-world natural scene images,
we need to first segment an image into regions so that each region
approximately corresponds to an object part. In our implementation,
we make use of Felzenszwalb and Huttenlocher’s
[5] approach to generate initial superpixels for an outdoor scene
image.We choose thismethod because it is very efficient and the
result of the method is comparable to the mean-shift [48] algorithm.
However, the initial superpixels are, in many cases, still
too noisy. To further improve the segmentation quality, we apply
a segment-merge method on the initial superpixels to merge the
small size regions (i.e., region size 0.03% of the image size)
with their neighbors.
Limitation of Our Method
Fig. 7 shows some bad examples of our method’s results.
There are still some mistakes that we would like to address in
the future. The segmentation of our POM is mainly based on
the geometric relationships between different object parts. This
requires obtaining the geometric properties (e.g., shape, size,
etc.) of object parts.We assume that object parts have nearly homogenous
surfaces, and hence, the uniform regions in an image
correspond to object parts. Although this assumption holds in
most cases, there are still some exceptions. For example, in
Fig. 7(a), the black car body is painted into different patterns.
As a result, the car body is oversegmented to many small parts.
Under this situation, our POM could not detect any special relationships
between the small parts and hence could not piece
them together. Similar situations can be found on the woman’s
clothing in Fig. 7(b) and the leopard in Fig. 7©.
CONCLUSION AND DISCUSSION
We have presented a novel image segmentation algorithm
for outdoor natural scenes. Our main contribution is that we develop
a POM. Our experimental results show that our proposed
method outperformed two competing state-of-the-art image
segmentation approaches (Gould09 [49] and global probability
of boundary [61]) and achieved good segmentation quality on
two challenging outdoor scene image data sets (GDS [52] and
BSDS [60]).