Page 1 of 11

Journal for Studies in Management and Planning

Available at

http://edupediapublications.org/journals/index.php/JSMaP/

e-ISSN: 2395-0463

Volume 02 Issue 12

December 2016

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 38

Region of exceptional detection by fusion of compactness

based on local prevalence and contrast

Ganuthala Upendar Reddy,2. Mr.P.V.Vara Prasad Rao

1PG Scholar, Department of ECE, SLC's Institute of Engineering and Technology, Piglipur Village, Hayathnagar Mandal, Near

Ramoji Film City, Ranga Reddy District, Hyderabad, Telangana

2Assosciate Professor, Department of ECE, SLC's Institute of Engineering and Technology, Piglipur Village, Hayathnagar

Mandal, Near Ramoji Film City, Ranga Reddy District, Hyderabad, Telangana

Abstract—Salient region detection is a

challenging problem and an important topic

in computer vision. It has a wide range of

applications, such as object recognition and

segmentation. Many approaches have been

proposed to detect salient regions using

different visual cues, such as compactness,

uniqueness, and objectness. However, each

visual cue-based method has its own

limitations. After analyzing the advantages

and limitations of different visual cues, we

found that compactness and local contrast

are complementary to each other. In

addition, local contrast can very effectively

recover incorrectly suppressed salient

regions using compactness cues. Motivated

by this, we propose a bottom-up salient

region detection method that integrates

compactness and local contrast cues.

Furthermore, to produce a pixel-accurate

saliency map that more uniformly covers the

salient objects, we propagate the saliency

information using a diffusion process. Our

experimental results on four benchmark

data sets demonstrate the effectiveness of the

proposed method. Our method produces

more accurate saliency maps with better

precision-recall curve and higher F- Measure than other 19 state-of-the-arts

approaches on ASD, CSSD, and ECSSD

data sets.

I. INTRODUCTION

Visual attention is an important mechanism

of the human visual system. It filters out

redundant visual information and effectively

selects highly relevant subjects, which are

called the salient objects. Visual attention is

considered to involve two mechanisms:

stimulus driven [1] and task driven . The

stimulus-driven mechanism is often called

bottom-up, and is fast, involuntary, and

purely based low-level visual stimuli. The

task-driven mechanism is called top-down,

and is based on high-level information such

as prior knowledge of the task, emotions,

and expectations. Accordingly,

Page 2 of 11

Journal for Studies in Management and Planning

Available at

http://edupediapublications.org/journals/index.php/JSMaP/

e-ISSN: 2395-0463

Volume 02 Issue 12

December 2016

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 39

computational visual attention methods can

be categorized into bottom-up and top-down

methods. In this paper, we focus on bottom- up salient region detection tasks. Salient

region detection methods aim to completely

highlight entire objects of interest and

sufficiently suppress background regions.

Their output can be used for numerous

computer vision problems such as image

classification object detection and

recognition image compression and image

segmentation As a fundamental computer

vision task, salient region detection has been

extensively studied over the past few years,

and a number of algorithms have been

proposed Most bottom-up salient region

detection methods rely on visual cues to

consistently separate the salient object and

background. These cues include uniqueness

compactness and background.

Most uniqueness-based methods use low- level features of the image (such as

intensity, color, and orientation) to

determine the contrast between image pixels

or regions and their surroundings. According

to the contrastive reference regions, these

methods can be roughly divided into local- and global contrast-based methods. Local

contrast-based methods consider the

uniqueness of pixels (or superpixels, image

regions) with respect to their surrounding

regions or local neighborhoods, whereas

global contrast-based methods consider

contrast relationships over the entire image.

Unlike uniqueness-based methods, which

consider the uniqueness of low-level

features in the feature space, compactness

based methods consider the spatial variance

of features. Ideally, salient pixels (or

superpixels, image regions) tend to have a

small spatial variance in the image space,

whereas the background is distributed over

the entire image and has a high spatial

variance. Background based methods use

boundary and connectivity priors derived

from common backgrounds in natural

images . These methods are primarily

motivated by the psychophysical

observations that salient objects seldom

touch the image boundary, and most

background regions can be easily connected

to each other. Although the above- mentioned methods have achieved good

results in some aspects, each method has its

own limita-

Page 3 of 11

Journal for Studies in Management and Planning

Available at

http://edupediapublications.org/journals/index.php/JSMaP/

e-ISSN: 2395-0463

Volume 02 Issue 12

December 2016

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 40

Fig. 1. Visual limitations of different

methods. (a) Input image. (b) Ground truth

salient regions. (c) Saliency maps using

local contrast based method . (d) Saliency

maps using global contrast based method

[7]. (e) Saliency maps using compactness

based method . (f) Saliency maps using

background based method . (g) Our method.

tions.

For example, Fig. 1 illustrates the saliency

detection results using four state-of-the-art

methods Figure 1(c) shows that the local

contrast-based method tends to highlight the

salient object’s edges instead of uniformly

propagating the saliency to the interior. The

global contrast based method sometimes

produces high saliency values for non- salient regions, especially for regions with

complex patterns or rare background

distractors. This is shown in the first

example of Fig. 1(d), where some grass

regions in the background are highlighted. A

typical limitation of the compactness based

method is that some salient regions may be

wrongly suppressed when the foreground

objects and background are similar. In the

second example of Fig. 1(e), the inner

smooth parts of the clock are wrongly

suppressed. Finally, background based

methods can perform very well. However,

they fail when the salient objects touch the

image boundary, as illustrated in the last two

examples of Fig. 1(f). From the above

discussion, we can conclude that single

visual cue based salient region detection

methods all have their own limitations. To

determine these limitations, different visual

cues should be integrated into a unified

framework. Motivated by this approach,

some methods integrate multiple visual cues.

Perazzi et al. [9] proposed a saliency filters

method, which unifies uniqueness and

compactness (of the spatial distribution) into

a single, high-dimensional, Gaussian

filtering framework. However, global

contrast and compactness based methods

have difficulty distinguishing between

similar colors in the foreground and

background. Consequently, the saliency

filters method fails when foreground objects

and the background are similar (e.g., the

second example in Fig. 1(e)). In this work,

we integrated local contrast and

compactness visual cues to generate saliency

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