Soft Computing

, Volume 23, Issue 8, pp 2485–2498 | Cite as

Saliency detection in stereoscopic images using adaptive Gaussian Kernel and Gabor filter

  • Y. RakeshEmail author
  • K. Sri Rama Krishna


So far a number of saliency detection designs have been developed for various applications in multimedia scenario. However, certain applications of stereoscopic imaging necessitate enhancing saliency detection designs to extract the salient areas accurately. Saliency detection strategy still faces many challenges like complexity in natural images as well as lesser-scale patterns in salient objects. This paper is set to obtain the saliency detection technique in two stages: feature extraction and depth saliency detection. Gaussian Kernel model is used to extract the features and Gabor filter to get the depth saliency map. This paper optimizes two coefficients namely feature difference among image patches \( U \), from feature evaluation and fine scale \( c \), from which the accurate detection can be achieved. For this, a renowned optimization algorithm named grey wolf optimization (GWO) is used, which is a recently proposed intelligent optimization method inspired by hunting behavior of grey wolves. The saliency detection model detection model is implemented in MATLAB 2015a. To the next of implementation, the performance of the proposed model, GWO-3-dimensional saliency mapping is compared over other the conventional algorithms in terms of receiver operator curve, Pearson correlation coefficient, Kullback–Leibler divergence and area under the curve. The overall performance analysis in terms of above measures and a valuable statistical analysis are carried out for validating the effectiveness of proposed model. The entire analysis proves that the proposed saliency detection performs better than the conventional models.


Stereoscopic imaging Saliency detection Feature extraction Depth saliency detection Grey wolf optimization 


Compliance with ethical standards

Conflict of interest

The author declares that they have no conflict of interest.

Research involving human participants and/or animals

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008.

Informed consent

Informed consent was obtained from all patients for being included in the study.


  1. Abualigah LM, Khader AT, Hanandeh ES (2018a) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125CrossRefGoogle Scholar
  2. Abualigah LM, Khader AT, Hanandeh ES (2018b) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071CrossRefGoogle Scholar
  3. Al-Sai ZA, Abualigah LM (2017) Big data and E-government: a review. In: 8th International conference on information technology (ICIT), pp 580–587Google Scholar
  4. Amin F, Fahmi A, Abdullah S, Ali A, Ahmed R, Ghani F (2017) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. J Intell Fuzzy Syst 34:2401–2416CrossRefGoogle Scholar
  5. An H, Wang D, Pan Z, Chen M, Wang X (2018) Text segmentation of health examination item based on character statistics and information measurement. CAAI Trans Intell Technol 3(1):28–32CrossRefGoogle Scholar
  6. Bramhe SS, Dalal A, Tajne D, Marotkar D (2015) glass shaped antenna with defected ground structure for cognitive radio application. Comput Commun Control Autom 13:330–333Google Scholar
  7. Cao Y, Kang K, Zhang S, Zhang J, Wang Z (2016) Automatic tag saliency ranking for stereo images. Neuro Comput 172:9–18Google Scholar
  8. Chen W, Sun T, Li M, Jiang H, Zhou C (2014) A new image co-segmentation method using saliency detection for surveillance image of coal miners. Comput Electr Eng 40(8):227–235CrossRefGoogle Scholar
  9. Chen Y, Pan Y, Song M, Wang M (2015) Improved seam carving combining with 3D saliency for image retargeting. Neuro Comput 151:645–653Google Scholar
  10. Cong R, Lei J, Zhang C, Huang Q, Cao X, Hou C (2016) Saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion. IEEE J Magaz 23(6):819–823Google Scholar
  11. Dinh CV, Leitner R, Paclik P, Loog M, Duin RPW (2011) SEDMI: saliency based edge detection in multispectral images. Image Vis Comput 29(8):546–556CrossRefGoogle Scholar
  12. Divyashree K, Likhithesh MD, Arpitha M, Madan Raj KS, Raghu S (2015) Proof collection from car black box using smart phone for accident detection. Emergency 2(3):9Google Scholar
  13. Du H, Liu Z, Song H, Mei L, Xu Z (2016) Improving RGBD saliency detection using progressive region classification and saliency fusion. IEEE J Magaz 4:8987–8994Google Scholar
  14. Fahmi A, Amin F (2018) Precursor selection for sol-gel synthesis of titanium carbide nanopowders by a new hesitant cubic fuzzy multi-attribute group decision-making model. New Math Nat Comput. Google Scholar
  15. Fahmi A, Abdullah S, Amin F, Siddque N, Ali A (2017) Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems. J Intell Fuzzy Syst 33:3323–3337CrossRefGoogle Scholar
  16. Fahmi A, Abdullah S, Amin F, Ali A (2018a) Weighted average rating (war) method for solving group decision making problem using triangular cubic fuzzy hybrid aggregation (Tcfha). Punjab Univ J Math 50(1):23–34MathSciNetGoogle Scholar
  17. Fahmi A, Abdullah S, Amin F, Ali A, Khan WA (2018b) Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making. J Intell Fuzzy Syst 35(2):2485–2499CrossRefGoogle Scholar
  18. Fahmi A, Abdullah S, Amin F, Khan MSA (2018c) Trapezoidal cubic fuzzy number einstein hybrid weighted averaging operators and its application to decision making. Soft Comput. Google Scholar
  19. Fahmi A, Amin F, Abdullah S, Ali A (2018d) Cubic fuzzy Einstein aggregation operators and its application to decision making. Int J Syst Sci. MathSciNetGoogle Scholar
  20. Fang Y, Wang J, Narwaria M, Le Callet P, Lin W (2014a) Saliency detection for stereoscopic images. IEEE J Magaz 23(6):2625–2636MathSciNetzbMATHGoogle Scholar
  21. Fang Y, Wang J, Narwaria M, Le Callet P, Lin W (2014b) Saliency detection for stereoscopic images. IEEE Trans Image Process 23(6):2625, 2636MathSciNetCrossRefzbMATHGoogle Scholar
  22. Fang Y, Lin W, Fang Z, Lei J, Le Callet P, Yuan F (2014c) Learning visual saliency for stereoscopic images. In: IEEE conference publications, pp 1–6Google Scholar
  23. Fang Y, Wang J, Yuan Y, Lei J, Le Callet P (2016) Saliency-based stereoscopic image retargeting. Inf Sci 372:347–358CrossRefGoogle Scholar
  24. Fang Y, Lei J, Li J, Xu L, Le Callet P (2017) Learning visual saliency from human fixations for stereoscopic images. Neuro Comput 266:284Google Scholar
  25. Harel J, Koch C, Perona P (2006) Graph-based visual saliencyGoogle Scholar
  26. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259CrossRefGoogle Scholar
  27. Jiang Q, Shao F, Jiang G, Yu M, Yu C (2015) A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency. Signal Process Image Commun 38:57–69CrossRefGoogle Scholar
  28. Jiang G, Xu H, Yu M, Luo T, Zhang Y (2017) Stereoscopic image quality assessment by learning non-negative matrix factorization-based color visual characteristics and considering binocular interactions. J Vis Commun Image Represent 46:269–279CrossRefGoogle Scholar
  29. Jung C, Cao L, Liu H, Kim J (2015) Visual comfort enhancement in stereoscopic 3D images using saliency-adaptive nonlinear disparity mapping. Displays 40:17–23CrossRefGoogle Scholar
  30. Li S, Zeng C, Liu S, Fu Y (2017a) Merging fixation for saliency detection in a multilayer graph. Neurocomputing 230:173–183CrossRefGoogle Scholar
  31. Li C, Zhang Y, Tu W, Jun C, Liang H, Yu H (2017b) Soft measurement of wood defects based on LDA feature fusion and compressed sensor images. J For Res 28(6):1285–1292CrossRefGoogle Scholar
  32. Lin H, Lin C, Zhao Y, Wang A (2017) 3D saliency detection based on background detection. J Vis Commun Image Represent 48:238–253CrossRefGoogle Scholar
  33. Liu Z, Le Meur O, Borji A, Li H (2015) Special issue on recent advances in saliency models, applications and evaluations. Signal Process Image Commun 38:1–2CrossRefGoogle Scholar
  34. Liu Y, Yang J, Meng Q, Lv Z, Gao Z (2016) Stereoscopic image quality assessment method based on binocular combination saliency model. Signal Process 125:237–248CrossRefGoogle Scholar
  35. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  36. Pedersen MEH, Chipperfield AJ (2010) Simplifying particle swarm optimization. Appl Soft Comput 10:618–628CrossRefGoogle Scholar
  37. Peng R, Varshney PK (2015) A human visual system-driven image segmentation algorithm. J Vis Commun Image Represent 26:66–79CrossRefGoogle Scholar
  38. Qi J, Dong S, Huang F, Lu H (2017) Saliency detection via joint modeling global shape and local consistency. Neurocomputing 222:81–90CrossRefGoogle Scholar
  39. Roy RG, Baidya D (2018) Speed control of DC motor using fuzzy-based intelligent model reference adaptive control scheme. Adv Commun 462:729–735Google Scholar
  40. Shankar A, Jaisankar N (2016) Security enabled cluster head selection for wireless sensor network using improved firefly optimization. In: International conference on soft computing and pattern recognition, pp 176–192Google Scholar
  41. Song H, Liu Z, Du H, Sun G, Le Meur O, Ren T (2017) depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning. IEEE J Magaz 26(9):4204–4216MathSciNetzbMATHGoogle Scholar
  42. Suganya P, Iyapparaja M, Navaneethan MC, Meenatchi S, Kumar PJ (2018) A privacy-preserving secure access control mechanism in cloud. J Adv Res Dyn Control Syst 13:844–850Google Scholar
  43. Wang Z, Xu G, Wang Z, Zhu C (2016) Saliency detection integrating both background and foreground information. Neurocomputing 216:468–477CrossRefGoogle Scholar
  44. Wang W, Shen J, Yu Y, Ma K-L (2017) Stereoscopic thumbnail creation via efficient stereo saliency detection. IEEE J Magaz 23(8):2014–2027Google Scholar
  45. Zhang C, Tao Z, Wei X, Cao X (2015a) A flexible framework of adaptive method selection for image saliency detection. Pattern Recognit Lett 63:66–70CrossRefGoogle Scholar
  46. Zhang X, Wang Y, Zhang J, Hu L, Wang M (2015b) Light field saliency vs. 2D saliency: a comparative study. Neuro Comput 166:389–396Google Scholar
  47. Zhang Y, Zhang F, Guo L (2016) Saliency detection by selective colour features. Neurocomputing 203:34–40CrossRefGoogle Scholar
  48. Zhou Y, Li X, Gao L (2013) A differential evolution algorithm with intersect mutation operator. Appl Soft Comput 13:390–401CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Usha Rama College of Engineering and Technology (URCET)GannavaramIndia
  2. 2.Velgapudi Ramakrishna Siddhartha Engineering College (VRSEC)VijayawadaIndia

Personalised recommendations