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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

DOI 10.1109/CVPR.2013.271

2081

2013 IEEE Conference on Computer Vision and Pattern Recognition

DOI 10.1109/CVPR.2013.271

2081

2013 IEEE Conference on Computer Vision and Pattern Recognition

DOI 10.1109/CVPR.2013.271

2083

The basis of most saliency detection algorithms can

date back to the feature integration theory [43] which

posits that diﬀerent kinds of attention are responsi-

ble for binding various features into consciously ex-

perienced wholes. Later, a computational attention

model built on a biologically-plausible architecture is

proposed in [28] and completely implemented in [22].

It represents the input image from the color, intensity

and orientation channels, and computes three conspicu-

ity (saliency) maps using center-surround diﬀerences,

which are combined together to form the ﬁnal master

saliency map.

Recently, a lot of research eﬀorts have been made to

design various saliency features characterizing salient

objects or regions. Most works essentially follow

the center-surround diﬀerence (or contrast) framework.

The discriminant center-surround hypothesis is ana-

lyzed in [15, 16]. Color histograms, computed to repre-

sent the center and the surround, are used to evaluate

the center-surround dissimilarity [31]. An information

theory perspective is introduced to yield a sound math-

ematical formulation, computing the center-surround

divergence based on feature statistics [27].

The center-surround diﬀerence framework is also in-

vestigated to compute the saliency from region-based

image representation. In [23], the diﬀerence between

the color histogram of a region and its immediately

neighboring regions are used to evaluate the saliency

score. The global contrast based approach [11], com-

puting the saliency map by comparing each region with

others, aims to directly compute the global uniqueness.

Based on the regional contrast, element color unique-

ness and spatial distribution are introduced to evaluate

the saliency scores of regions [38]. The saliency map is

generated by propagating the saliency scores of regions

to the pixels.

Many other models are also proposed for saliency

computation. Center-bias, i.e. the salient object usu-

ally lies in the center of an image, is investigated

in [23, 50]. Object prior, such as connectivity prior [45],

concavity context [34], auto-context cue [48], and the

background prior [53] are also studied for saliency com-

putation. Example-based approaches, searching for

similar images of the input, are developed for salient

object detection [35, 49]. A graphical model is pro-

posed to fuse generic objectness and visual saliency to-

gether to detect objects [10]. A low rank matrix recov-

ery scheme is proposed for salient object detection [41].

A top-down approach via joint conditional random

ﬁelds and dictionary learning is introduced [54]. The

stereopsis is leveraged for saliency analysis [37]. Be-

sides, spectral analysis in the frequency domain is used

to detect salient regions [1, 20]

Additionally, there are several works directly check-

ing if an image window contains an object. The generic

objectness measure is deﬁned by combining several im-

age cues to quantify the possibility that a window con-

tains an object [2]. A category independent object de-

tection cascade, which uses superpixel boundary inte-

gral, edge distribution and window symmetry to de-

scribe objectness, is learnt to rank a number of object

window candidates [39]. Salient object detection by

composition [13] checks if the content within an win-

dow can be composed by neighbor regions. A random

forest regression approach is adopted to directly regress

the object rectangle from the saliency map [50].

Eye ﬁxation prediction, another visual saliency re-

search direction, also attracts a lot of interests [7, 24].

Recent developments include using isocentric curved-

ness and color [44], adopting image histogram [32],

quaternion-based spectral analysis [40], utilizing depth

cues [30], multitask sparsity pursuit [29], statistically

modeling [42], exploring patch rarities [6], combing

bottom-up and top-down features [5], task-speciﬁc vi-

sual attention [8] and so on. There are some other

saliency deﬁnitions, e.g. context-aware saliency detec-

tion [18] aiming to detect the image regions that rep-

resent the scene.

Our proposed approach diﬀers from existing algo-

rithms on two points. In term of the saliency features,

we compute a contrast vector instead of a contrast

value used in the existing algorithms for a region. Par-

ticularly, a novel feature vector is introduced to charac-

terize the background. Our approach is also unique in

the learning strategy. In contrast to existing learning

algorithms that perform saliency integration by com-

bining saliency maps computed from diﬀerent types of

features, e.g. [2, 10, 31], our approach learns to directly

integrate feature vectors to compute the saliency map.

The closely related approach [26] which also learns to

integrate the saliency features is a pixel-based algo-

rithm, while our approach is region-based that per-

forms multi-level estimation and can capture non-local

contrast. Moreover, we introduce a novel regional fea-

ture vector to characterize the background. Another

one [36] touches the discriminative feature integration

lightly without presenting a deep investigation and it

only considers the regional property descriptor. The

recent learning approach [33] aims to predict eye ﬁxa-

tion, while our approach is for salient object detection

and moreover, we solve the problem by introducing and

exploring multi-level regional descriptors. The discrim-

inative feature fusion has also been studied in image

classiﬁcation [14], which learns the adaptive weights

of features according to the classiﬁcation task to bet-

ter distinguish one class from others. Our approach

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