Salient Object Detection: A Discriminative Regional Feature
Integration Approach
Huaizu Jiang
†
Jingdong Wang
‡
Zejian Yuan
†
Yang Wu
§
Nanning Zheng
†
Shipeng Li
‡
†
Xi’an Jiaotong University
‡
Microsoft Research Asia
§
Kyoto University
https://sites.google.com/site/jianghz88/saliency_drfi
Abstract
Salient object detection has been attracting a lot of
interest, and recently various heuristic computational
models have been designed. In this paper, we regard
saliency map computation as a regression problem. Our
method, which is based on multi-level image segmenta-
tion, uses the supervised learning approach to map the
regional feature vector to a saliency score, and finally
fuses the saliency scores across multiple levels, yielding
the saliency map. The contributions lie in two-fold.
One is that we show our approach, which integrates
the regional contrast, regional property and regional
backgroundness descriptors together to form the master
saliency map, is able to produce superior saliency maps
to existing algorithms most of which combine saliency
maps heuristically computed from different types of fea-
tures. The other is that we introduce a new regional fea-
ture vector, backgroundness, to characterize the back-
ground, which can be regarded as a counterpart of the
objectness descriptor [2]. The performance evaluation
on several popular benchmark data sets validates that
our approach outperforms existing state-of-the-arts.
1. Introduction
Visual saliency has been a fundamental problem in
neuroscience, psychology, neural systems, and com-
puter vision for a long time. It is originally a task of
predicting the eye-fixations on images, and recently has
been extended to identifying a region containing the
salient object, which is the focus of this paper. There
are various applications for salient object detection, in-
cluding object detection and recognition [25, 46], im-
age compression [21], image cropping [35], photo col-
lage [17, 47], dominant color detection [51, 52] and so
on.
The study on human visual systems suggests that
the saliency is related to uniqueness, rarity and sur-
prise of a scene, characterized by primitive features
like color, texture, shape, etc. Recently a lot of efforts
have been made to design various heuristic algorithms
to compute the saliency [1, 6, 11, 15, 18, 27, 31, 34, 38].
In this paper, we regard saliency estimation as a
regression problem, and learn a regressor that directly
maps the regional feature vector to a saliency score.
Our approach consists of three main steps. The first
one is multi-level segmentation, which decomposes the
image to multiple segmentations from a fine level to
a coarse one. Second, we conduct a region saliency
computation step with a random forest regressor that
maps the regional features to a saliency score. Last, a
saliency map is computed by fusing the saliency maps
across multiple levels of segmentations.
The key contributions lie in the second step, region
saliency computation. Unlike most existing algorithms
that compute saliency maps heuristically from various
features and combine them to get the saliency map,
which we call saliency integration, we learn a random
forest regressor that directly maps the feature vector
of each region to a saliency score, which we call dis-
criminative regional feature integration (DRFI). This
is a principle way in image classification [19], but rarely
studied in salient object detection. It turns out that the
learnt regressor is able to automatically pick discrimi-
native features rather than heuristically hand-crafting
special features for saliency. On the other hand, we
also introduce a new descriptor, called backgroundness,
to discriminate the background from the object, which
can be considered as a counterpart of the objectness
descriptor [2].
1.1. Related work
The following gives a review of salient object detec-
tion (segmentation) algorithms that are related to our
approach. A comprehensive survey of salient object
detection can be found from [9]. The review on visual
attention modeling [7] also includes some analysis on
salient object detection.
2013 IEEE Conference on Computer Vision and Pattern Recognition
1063-6919/13 $26.00 © 2013 IEEE
DOI 10.1109/CVPR.2013.271
2081
2013 IEEE Conference on Computer Vision and Pattern Recognition
1063-6919/13 $26.00 © 2013 IEEE
DOI 10.1109/CVPR.2013.271
2081
2013 IEEE Conference on Computer Vision and Pattern Recognition
1063-6919/13 $26.00 © 2013 IEEE
DOI 10.1109/CVPR.2013.271
2083
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