A Benchmark of Computational Models of Saliency to Predict

Human Fixations in Videos

Shoaib Azam

, Syed Omer Gilani

, Moongu Jeon

, Rehan Yousaf

and Jeong Bae Kim

School of Information and Communication, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea

National University of Sciences and Technology, Islamabad, Pakistan

Department Management of Technology, Pukyong National University, Busan, Republic of Korea

Keywords: Saliency Benchmark of Videos, Scoring Metrics, Fixation Maps.

Abstract: In many applications of computer graphics and design, robotics and computer vision, there is always a need

to predict where human looks in the scene. However this is still a challenging task that how human visual

system certainly works. A number of computational models have been designed using different approaches

to estimate the human visual system. Most of these models have been tested on images and performance is

calculated on this basis. A benchmark is made using images to see the immediate comparison between the

models. Apart from that there is no benchmark on videos, to alleviate this problem we have a created a

benchmark of six computational models implemented on 12 videos which have been viewed by 15 observers

in a free viewing task. Further a weighted theory (both manual and automatic) is designed and implemented

on videos using these six models which improved Area under the ROC. We have found that Graph Based

Visual Saliency (GBVS) and Random Centre Surround Models have outperformed the other models.

1 INTRODUCTION

Saliency has been discussed and implemented in so

many ways on images and many computational

models have been defined in this respect. Each

computational model is at best as an individual

technique and also has proven the psychology of the

human visual system. There is no such method to

judge an individual technique until and unless it is

compared to some standard or state of the art. But

comparing different models gives you the basic idea

about how good a model is and how bad a model is.

Previously, many people have done comparison

between models and got results to improve the

technique but this has been done only on static images

(Judd et al., 2012); (Privitera and Stark, 2000);

(Parkhurst et al., 2002); (Ouerhani, 2003); (Elazary

and Itti, 2008); (Henderson et al., 2007); (Bruce and

Tsotsos, 2006); (Itti, 2005); (Peters et al., 2005);

(Yubing et al., 2010); (Yubing et al., 2011). Our goal

is to pick up certain saliency models (Borji and Itti,

2010) and apply them on videos. After applying these

models on the videos we will compare these models

with each other and generate fixation map, through

which we compute the Area Under Receiver

Operating Characteristic Curve (AUC score) (Borji et

al., 2013a); (Borji et al., 2013b).

(AUC) and set our own benchmark in which we

will only be dealing with videos. Further, we have

designed a weighted algorithm for saliency

computation. It includes both manual and automatic

weight assigning. For this AUC is also calculated.

For performing our goal we have taken a dataset

(Hadizadeh et al., 2012) which includes 15 observer

and 6 models which we will be comparing. These

models will be applied on 12 videos having the

following details (name and frames) are Foreman

(300 frames), Bus (150 frames), City (300 frames),

Crew (300 frames), Flower Garden (250 frames),

Mother and Daughter (300 frames), Soccer (300

frames), Stefan (90 frames), Mobile Calendar (300

frames), Harbor (300 frames), Hall Monitor (300

frames) and Tempete (260 frames).The dataset which

we are using is generated with a free viewing task, it

states that the participants are not restricted to see

particular object in the scene.

No doubt the models are very near to the human

visual system but a small difference still shows there

is a big room for improvement.

134

Azam, S., Gilani, S., Jeon, M., Yousaf, R. and Kim, J.

A Benchmark of Computational Models of Saliency to Predict Human Fixations in Videos.

DOI: 10.5220/0005678701340142

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP, pages 134-142

ISBN: 978-989-758-175-5

2 CONTRIBUTION

We have made the following contribution to the

computational models and the human fixations:

 We have used only 6 saliency models (which are

the major attention models in last ten years).

 We have applied these models on 12 videos.

 For each video with having specified number of

frames we have computed their saliency maps

with all the said 6 models.

 We have used the eye tracking dataset of 15

observers for creating the fixation map for each

video.

 We have calculated the AUC (Borji et al., 2013a);

(Borji et al., 2013b) score as scoring metric for all

the videos, each containing specified frames.

 Most importantly we have designed our own

“weighted optimal algorithm” which assigns

weight to the models automatically also manually

as “user weighted algorithm” and computed the

AUC (Borji et al., 2013a); (Borji et al., 2013b)

scores, hence giving out weighted combination of

models showing the best possible results.

3 SALIENCY MAP AND

COMPUTATIONAL MODELS

All the computational models follow the same

procedure in computing the saliency map. The main

ides was first introduced by Itti and Koch (Koch and

Ullman, 1985). The saliency map is basically

generated on the basis of features in parallel and by

combining the features values gives us the saliency

map.

To compute different features at different scales

the model computes many pyramids of the input

image. Most commonly used image features are

intensity, colour and orientation. These features are

again divided into sub features. The feature maps are

generated through centre surround method or

difference of Gaussian (DoG) (Young, 1987). These

features map are added up and finally normalized and

weighted combined giving up the saliency map. This

technique is purely bottom up. Models do use top

down technique too (Borji and Itti, 2010).

In videos models are applied to each frame for

computing the saliency map. Then these frames are

again synchronized to the videos. The procedure for

generating the saliency map varies a little bit

according to the each computational model. But the

main idea remains the same.

The models which we are using for generating

saliency map are as follows.

1) The first model which we are using is by Achanta

et al., (2008). This model uses a simple technique

which extracts objects from the background. This

is done through subtraction of common part

which is the background.

2) Secondly we have context aware saliency

(Goferman et al., 2010) model. This model

emphasizes on locating the most important part of

the image which has the main contents of

information but not just salient object.

3) Saliency detection based on wavelet (Nevrez et

al., 2013) is the third model. This model focuses

on extracting the low level features through

wavelet transform then it creates the feature map

which clearly points out the features such as edges

and texture.

4) Fourthly we have graph based visual saliency

model (Harel et al., 2007) (GBVS) is a two steps

procedure which contains making of activation

maps on specific feature channels and then

normalizes it according to the others map which

it has to combine with.

5) The fifth model we have is called random

surround (Vikram et al., 2012) model. This model

follows a simple procedure to calculate the image

saliencies which consists of computing local

saliencies over some random regions in the

rectangular shape according to the task interest.

Then the Gaussian filter is applied to remove the

noise from the images. Colour space is generated

and divided in to L*,a* and b* channels. Saliency

maps are generated in these channels. Besides n

random sub windows are produced over each of

L*,a* and b* channel. The final saliency map is

generated by fusing the Euclidean norm of

saliency calculated in three above channels.

6) The last model is the spatio-temporal saliency

detection through Fourier transforms (Guo et al.,

2008). In this model the technique uses the phase

spectrum to calculate the saliency map. It is

similar in procedure to the amplitude spectrum

used by spectral residual model but varies in the

component of focus that is phase.

4 PROPOSED METHODOLOGY

Our proposed methodology consists of 6 saliency

computational models which we are applying on

videos and computing their saliency maps. The

computational models which we are using are Spatio-

A Benchmark of Computational Models of Saliency to Predict Human Fixations in Videos

135

temporal Saliency Detection through Fourier

transform (Guo et al., 2008), Random Surround

model (Vikram et al., 2012), Saliency detection on

Wavelets (Nevrez et al., 2013), Graph based Visual

Saliency Model (Harel et al., 2007), Context Aware

Saliency Model (Goferman et al., 2010) and Achanta

et al., (2008) model. These 6 models are applied to 12

video sets each containing specified frames. For

instance, the first model is applied to a 12 video which

gives us the saliency maps according to number of

frames of the video and by using the similar pattern

all the models have been applied to all the videos each

giving us saliency maps depending on their number

of frames.

Now the dataset (Hadizadeh et al., 2012) which

we are using have been viewed by 15 observers in a

free viewing scenario and their eye tracking data is

used to compute fixation map for frames of a video.

These fixation maps are computed for all the 12

videos sets. So we have the saliency maps for all the

videos along with the fixation maps. These fixation

maps are then used for the calculation of AUC score.

We are using the Ali Borji’s (Borji et al., 2013a);

(Borji et al., 2013b) implementation to compute the

AUC score to compare the results of the machine

visual system and human visual system. We have

calculated the score for each video containing

different number of frames for all the 6 models

individually. This process gives us the comparison of

each model with another and its comparison with the

human visual system.

After computing the AUC score we have assigned

weights to the models. Mathematically weights are

using the following equation.

(1)

In the above equation S is final saliency map

computed, wi is weight assigned to model, Mi is the

respective model and n is the total number of models.

Here in our case n is equal to 6 as we have used six

models. There are two methods of assigning the

weights.

i) Automatic Weight Assigning

ii) Manual Assigning(User Assigning)

4.1 Automatic Weight Assigning

In automatic assigning there are three configurations

for assigning the weights automatically. In first

configuration of automatic weight assigning Model 1

has been assigned 0.8 weights and the rest of other

models have been assigned 0.5, then model 2 with 0.8

weight and the others with 0.5 and this process carry

on iteratively. Using this arrangement 6 combination

has been made. In the second configuration 0.5 and

0.2 are the weights assigned to the models in the

similar manner making 6 combinations. In the third

configuration 0.8 and 0.2 are the weights assigned to

the models again in the similar manner making 6

combinations. After weight assigning AUC score is

computed with the weighted saliency map for all the

videos. Basically we have chosen three types of

weights 0.8 (maximum weight), 0.5(middle weight)

and 0.2 (minimum weight).

4.2 Manual Weight Assigning

The second method for weight assigning is the

manual weight assigning method. In this procedure

the user enters the weights for each computational

model depending upon the result of model. The

process then computes the saliency map according to

weights assigned by the user under the perspective of

equation (1). After weight assigning, the AUC is used

as a scoring metric to evaluate the saliency maps.

Further, as we have used six models yet there is a

room to add more models and see which models has

fruitful result on videos.

5 FLOWCHART OF PROPOSED

METHODOLOGY

The following flow diagrams show the overall

procedure of proposed methodology.

6 RESULTS AND DISCUSSION

6.1 Saliency Models Results

The above results shows the saliency maps of six

videos which has been obtained by implementing six

models on them. By observing these models we can

say that the models whose results are clearer and

distinct may have better approximation or nearer to

human visual system, however this is not the case and

can be analysed by the results of AUC.

6.2 Model Performance for Videos

The above table shows the mean AUC score for the 6

models which have been computed on the 12 videos.

As it can be seen from the table that in different

videos different model has outperformed the other

models, but in most of the cases it is GBVS and

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Figure 1: Flowchart of Saliency Map Computation and User Defined and Automatic Weight Assigning.

A Benchmark of Computational Models of Saliency to Predict Human Fixations in Videos

137



Video 1 Video 2 Video 3 Video 4 Video 5 Video 6

Original

  

Achanta

  

Context





GBVS

  

Phase Fourier

 

Random Centre

  

Wavelets

 

Figure 2: Saliency maps from 6 models of 6 videos.

Table 1: Performance of all six models using AUC implemented on videos. Name of video is also given. For AUC, higher

values are better.

AUC Score of Models Implemented on Videos

S.NO Video Name Achanta Context GBVS Phase Fourier Random Surround Wavelets

1 Bus 0.4574 0.6700 0.7235 0.4825

0.7446

0.5504

2 City 0.6114 0.6460 0.7484 0.5425

0.7756

0.6418

3 Crew 0.5085 0.5344

0.5972

0.4843 0.5302 0.5407

4 Flower Garden 0.4346 0.5104

0.8283

0.4426 0.8156 0.5316

5 Foreman 0.5153 0.6667

0.7614

0.5193 0.6072 0.3410

6 Hall Monitor 0.4061 0.4390 0.4864 0.4816

0.4879

0.3479

7 Harbor 0.4846 0.3920

0.8234

0.4385 0.7339 0.5232

8 Mobile 0.3297 0.3495

0.6372

0.4777 0.6004 0.3914

9 Mother 0.4093 0.6467 0.6884 0.4906 0.6713

0.7111

10 Soccer 0.6764 0.3749 0.7108 0.4670

0.7616

0.5440

11 Stefan 0.5802 0.8743

0.9269

0.6349 0.8735 0.6037

12 Tampete 0.5150 0.7362 0.7287 0.6573

0.7415

0.6706

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Random Surround Model that outperformed the other

models. Like in videos (named) BUS and City,

Random Surround Model has the better AUC and it is

approaching to one. However, in videos (named)

Crew, Flower Garden and Foreman, GBVS has better

results as compared to other one. There is only one

video named Mother, in which Wavelet based model

has better results.

6.3 Manual Weight Assigning (User’s

Defined) Results

In manual weight assigning table, the mean AUC

scores of all videos except video 6, 8 and 10 have

been increased. This weight assigning is random

depending upon the result of models (shown to user)

before assigning weights. For this assigning we have

selected or assigned the following weights to the

models.

Table 2: Performance Score of Manual Weight Assigning

(AUC Score).

AUC Score of Manual Weight Assigning

S.NO Video Name AUC Score

1 Bus 0.7237

2 City 0.6877

3 Crew 0.6193

4 Flower Garden 0.7279

5 Foreman 0.7581

Hall Monitor 0.3603

7 Harbor 0.7818

Mobile 0.5797

9 Mother 0.7066

Soccer 0.3198

11 Stefan 0.8391

12 Tampete 0.7291

6.4 Automatic Weight Assigning

Results

The above tables show the mean AUC scores of IST,

SEC and THIRD configuration of automatic weight

assigning. The assigning has been made using the

probabilities as 0.8, 0.5 and 0.2 as maximum, medium

and minimum to the models in specified

configuration. Using this behaviour three

configuration has been made. If we see the IST

configuration of automatic weight assigning, the

weights are assigned as 0.8 and 0.5 in a manner that

that in first module (MODULE 1) the weights

assigned to models are shown in Table 7. Using the

same technique the modules of other two

configuration has been achieved.

By considering the AUC scores of IST, SEC and

THIRD configuration with the AUC scores of

original videos, we can see that there is significant

amount of increased. However in some cases, there is

decreased in the AUC scores of entire videos due to

error in obtaining the fixation data for it. This can be

seen in Video 6. So, by viewing it overall there is 20

% improvement in the AUC scores using automatic

weight assigning. However the individual increase in

some cases is above 50 %. But we are more concerned

by overall result. Depending upon this the best

configuration which gives this performance is SEC

configuration.

7 CONCLUSION

We have performed the comparison between 6

different computational models and their saliency

maps. We have also computed the AUC of the

saliency map for the accuracy of computational

models for videos.

We have designed the weighted algorithm for the

saliency maps and using this we have computed their

AUC score as well. The weighted method is for both

user defined weights and automatic weights

assigning, thus make the algorithm more flexible and

leaving a room for further enhancement and addition

of further models, comparison in future work

8 FUTURE WORK

The future work will include that more videos dataset

can be used for both parts. Further video dataset

where observer is explicitly asked to view a certain

object will be included, so that the performance of

computational model is being analysed in not free

viewing videos. More saliency computational models

will be used to predict the human fixation in videos.

ACKNOWLEDGEMENTS

This work was supported by Institute for Information

& communications Technology Promotion (IITP)

grant funded by the Korea Government (MSIP)

(No.B0101-15-0525, Development of global multi-

target tracking and event prediction techniques based

on real-time large-scale video analysis) and Centre

for Integrated Smart Sensors as Global Frontier

(CISS-2013M3A6A6073718).

A Benchmark of Computational Models of Saliency to Predict Human Fixations in Videos

139

Table 3: Weight Assigned to Models in Manual Weight Assigning.

Weight Assigned to Models

Models Achanta Context GBVS Phase Fourier Random Surround Wavelets

Weights(0-1) 0.5 0.6 0.8 0.4 0.7 0.8

Table 4: Performance Score of Automatic Weight Assigning of IST Configuration (AUC Score).

AUC Score of IST Configuration of Automatic Weight Assigning

S.NO Video Name Module 1 Module 2 Module 3 Module 4 Module 5 Module 6

1 Bus 0.7043 0.6922 0.6953 0.7097 0.6977 0.6968

2 City 0.6853 0.6848 0.6878 0.6898 0.6867 0.6799

3 Crew 0.6448 0.6409 0.6284 0.6300 0.6428 0.6239

4 Flower Garden 0.7378 0.7387 0.7252 0.7246 0.7335 0.7192

5 Foreman 0.7770 0.7726 0.7664 0.7791 0.7712 0.7690

6 Hall Monitor 0.3649 0.3573 0.3655 0.3728 0.3574 0.3543

7 Harbor 0.7698 0.7673 0.7671 0.7783 0.7778 0.7871

8 Mobile 0.5749 0.5706 0.5678 0.5817 0.5744 0.5636

9 Mother 0.6712 0.6790 0.6810 0.6834 0.7047 0.6896

10 Soccer 0.5451 0.5454 0.5449 0.5449 0.5451 0.5451

11 Stefan 0.8332 0.8339 0.8572 0.8411 0.8494 0.8734

12 Tampete 0.7235 0.7180 0.7189 0.7211 0.7315 0.7185

Table 5: Performance Score of Automatic Weight Assigning of SEC Configuration (AUC Score).

AUC Score of SEC Configuration of Automatic Weight Assigning

S.NO Video Name Module 1 Module 2 Module 3 Module 4 Module 5 Module 6

1 Bus 0.6948 0.7044 0.7063 0.7280 0.7139 0.6967

2 City 0.7015 0.6966 0.6996 0.7186 0.6927 0.6977

3 Crew 0.6510 0.6373 0.6208 0.6277 0.6374 0.6199

4 Flower Garden 0.7107 0.7428 0.7199 0.7274 0.7051 0.7278

5 Foreman 0.7522 0.7747 0.7779 0.7760 0.7821 0.7788

6 Hall Monitor 0.3683 0.3685 0.3631 0.3730 0.3746 0.3578

7 Harbor 0.7700 0.7581 0.7756 0.7490 0.7588 0.7801

8 Mobile 0.5873 0.6111 0.6023 0.5784 0.5880 0.6095

9 Mother 0.6850 0.7022 0.6817 0.6821 0.6812 0.6787

10 Soccer 0.5442 0.5449 0.5437 0.5447 0.5449 0.5444

11 Stefan 0.8483 0.8664 0.8631 0.8689 0.8693 0.8545

12 Tampete 0.7273 0.7291 0.7167 0.7396 0.7266 0.7270

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Table 6: Performance Score of Automatic Weight Assigning of THIRD Configuration (AUC Score).

AUC Score of THIRD Configuration of Automatic Weight Assigning

S.NO Video Name Module 1 Module 2 Module 3 Module 4 Module 5 Module 6

1 Bus 0.6936 0.7098 0.7001 0.6916 0.7021 0.7088

2 City 0.7089 0.6850 0.6999 0.6848 0.6833 0.6686

3 Crew 0.6284 0.6408 0.6341 0.6318 0.6306 0.6353

4 Flower Garden 0.7042 0.7296 0.7271 0.7224 0.7247 0.7222

5 Foreman 0.7648 0.7685 0.7710 0.7745 0.7641 0.7558

6 Hall Monitor 0.3546 0.3629 0.3626 0.3560 0.3660 0.3717

7 Harbor 0.7702 0.7717 0.7604 0.7797 0.7802 0.7688

8 Mobile 0.5756 0.5996 0.5880 0.6086 0.5867 0.5996

9 Mother 0.7121 0.6977 0.6958 0.7003 0.6602 0.6801

10 Soccer 0.5448 0.5451 0.5453 0.5455 0.5453 0.5456

11 Stefan 0.8635 0.8659 0.8738 0.8556 0.8628 0.8780

12 Tampete 0.7232 0.7255 0.7254 0.7094 0.7133 0.7198

Table 7: Complete Weight assigning in automatic weight assigning configuration. Example of IST configuration.

S. No Achanta Context GBVS Phase Fourier Random Surround Wavelets

Module 1 0.8 0.5 0.5 0.5 0.5 0.5

Module 2 0.5 0.8 0.5 0.5 0.5 0.5

Module 3 0.5 0.5 0.8 0.5 0.5 0.5

Module 4 0.5 0.5 0.5 0.8 0.5 0.5

Module 5 0.5 0.5 0.5 0.5 0.8 0.5

Module 6 0.5 0.5 0.5 0.5 0.5 0.8

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