Skip to main content

Advertisement

Log in

Salient object detection employing regional principal color and texture cues

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Saliency in a scene describes those facets of any stimulus that makes it stand out from the masses. Saliency detection has attracted numerous algorithms in recent past and proved to be an important aspect in object recognition, image compression, classification and retrieval tasks. The present method makes two complementary saliency maps namely color and texture. The method employs superpixel segmentation using Simple Linear Iterative Clustering (SLIC). The tiny regions obtained are further clustered on the basis of homogeneity using DBSCAN. The method also employs two levels of quantization of color that makes the saliency computation easier. Basically, it is an adaptation to the property of the human visual system by which it discards the less frequent colors in detecting the salient objects. Furthermore, color saliency map is computed using the center surround principle. For texture saliency map, Gabor filter is employed as it is proved to be one of the appropriate mechanisms for texture characterization. Finally, the color and texture saliency maps are combined in a non-linear manner to obtain the final saliency map. The experimental results along with the performance measures have established the efficacy of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation. In: International conference on computer vision systems. Springer, pp 66–75

  2. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: IEEE conference on computer vision and pattern recognition, 2009. cvpr 2009, IEEE, pp 1597–1604

  3. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282

    Article  Google Scholar 

  4. Chang KY, Liu T L, Chen H T, Lai SH (2011) Fusing generic objectness and visual saliency for salient object detection. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 914–921

  5. Chang KY, Liu TL, Lai SH (2011) From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model. In: 2011 IEEE conference on computer vision and pattern recognition (cvpr), IEEE, pp 2129–2136

  6. Chen T, Cheng MM, Tan P, Shamir A, Hu SM (2009) Sketch2photo: Internet image montage. ACM Trans Graph (TOG) 28(5):124

    Google Scholar 

  7. Chen Zh, Liu Y, Sheng B, Jn Liang, Zhang J, Yb Yuan (2016) Image saliency detection using gabor texture cues. Multimed Tools Appl 75(24):16,943–16,958

    Article  Google Scholar 

  8. Cheng MM, Mitra NJ, Huang X, Torr PH, Hu SM (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582

    Article  Google Scholar 

  9. Clausi DA, Jernigan ME (2000) Designing gabor filters for optimal texture separability. Pattern Recog 33(11):1835–1849

    Article  Google Scholar 

  10. Goferman S, Zelnik-Manor L, Tal A (2012) Context-aware saliency detection. IEEE Trans Pattern Anal Mach Intell 34(10):1915–1926

    Article  Google Scholar 

  11. Guo C, Zhang L (2010) A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19(1):185–198

    Article  MathSciNet  MATH  Google Scholar 

  12. Han J, Ngan KN, Li M, Zhang HJ (2006) Unsupervised extraction of visual attention objects in color images. IEEE Trans Circuits Syst Video Technol 16 (1):141–145

    Article  Google Scholar 

  13. Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Advances in neural information processing systems, pp 545–552

  14. Hiremath P, Pujari J (2008) Content based image retrieval using color boosted salient points and shape features of an image. Int J Image Process 2(1):10–17

    Google Scholar 

  15. Hou X, Zhang L (2007 ) Saliency detection: A spectral residual approach. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE, pp 1–8

  16. Hou X, Harel J, Koch C (2012) Image signature: highlighting sparse salient regions. IEEE Trans Pattern Anal Mach Intell 34(1):194–201

    Article  Google Scholar 

  17. Hu Y, Rajan D, Chia LT (2005) Robust subspace analysis for detecting visual attention regions in images. In: Proceedings of the 13th annual ACM international conference on multimedia, pp 716–724

  18. Huang H, Zhang L, Fu TN (2010) Video painting via motion layer manipulation. In: Computer graphics forum, vol 29. Wiley Online Library, pp 2055–2064

  19. 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):1254–1259

    Article  Google Scholar 

  20. Ji QG, Fang ZD, Xie ZH, Lu ZM (2013) Video abstraction based on the visual attention model and online clustering. Signal Process Image Commun 28 (3):241–253

    Article  Google Scholar 

  21. Jiang P, Ling H, Yu J, Peng J (2013) Salient region detection by ufo: Uniqueness, focusness and objectness. In: 2013 IEEE international conference on computer vision (ICCV). IEEE, pp 1976–1983

  22. Jonathan H, Christof K, Pietro P (2006) Graph-based visual saliency. In: Proceedings of the 20th annual conference on neural information processing systems, pp 545–552

  23. Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 2106–2113

  24. Klein D A, Frintrop S (2011) Center-surround divergence of feature statistics for salient object detection. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 2214–2219

  25. Kurita T, Otsu N, Abdelmalek N (1992) Maximum likelihood thresholding based on population mixture models. Pattern Recogn 25(10):1231–1240

    Article  Google Scholar 

  26. Li A, She X, Sun Q (2013) Color image quality assessment combining saliency and fsim. In: 5th international conference on digital image processing (ICDIP 2013), international society for optics and photonics, vol 8878, p 88780I

  27. Li J, Levine MD, An X, Xu X, He H (2013) Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans Pattern Anal Mach Intell 35(4):996–1010

    Article  Google Scholar 

  28. Li X, Li Y, Shen C, Dick A, Van Den Hengel A (2013) Contextual hypergraph modeling for salient object detection. In: 2013 IEEE international conference on computer vision (ICCV). IEEE, pp 3328– 3335

  29. Liu F, Gleicher M (2006) Region enhanced scale-invariant saliency detection. In: 2006 IEEE international conference on multimedia and expo. IEEE, pp 1477–1480

  30. Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum HY (2011) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33(2):353–367

    Article  Google Scholar 

  31. Lou J, Ren M, Wang H (2014) Regional principal color based saliency detection. PloS one 9(11):e112,475

    Article  Google Scholar 

  32. Ma YF, Zhang HJ (2003) Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the 11th ACM international conference on multimedia. ACM, pp 374–381

  33. Meger D, Forssen PE, Lai K, Helmer S, McCann S, Southey T, Baumann M, Little JJ, Lowe DG (2008) Curious george: an attentive semantic robot. Robot Auton Syst 56(6):503–511

    Article  Google Scholar 

  34. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607

    Article  Google Scholar 

  35. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  36. Perazzi F, Krahenbuhl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 733–740

  37. Portilla J, Navarro R, Nestares O, Tabernero A (1996) Texture synthesis-by-analysis method based on a multiscale early-vision model. Opt Eng 35(8):2403–2417

    Article  Google Scholar 

  38. Ren YF, Mu ZC (2014) Salient object detection based on global contrast on texture and color. In: 2014 international conference on machine learning and cybernetics (ICMLC), vol 1. IEEE, pp 7–12

  39. Rosin PL (2009) A simple method for detecting salient regions. Pattern Recogn 42(11):2363–2371

    Article  MATH  Google Scholar 

  40. Rutishauser U, Walther D, Koch C, Perona P (2004) Is bottom-up attention useful for object recognition? In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004, CVPR 2004, vol 2. IEEE, pp II–II

  41. Sharma G, Jurie F, Schmid C (2012) Discriminative spatial saliency for image classification. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3506–3513

  42. Shen X, Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 853–860

  43. Siagian C, Itti L (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell 29(2):300–312

    Article  Google Scholar 

  44. Tatler BW (2007) The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. J Vis 7(14):4–4

    Article  Google Scholar 

  45. Tian H, Fang Y, Zhao Y, Lin W, Ni R, Zhu Z (2014) Salient region detection by fusing bottom-up and top-down features extracted from a single image. IEEE Trans Image Process 23(10):4389–4398

    Article  MathSciNet  MATH  Google Scholar 

  46. Valenti R, Sebe N, Gevers T (2009) Image saliency by isocentric curvedness and color. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 2185–2192

  47. Wang P, Wang J, Zeng G, Feng J, Zha H, Li S (2012) Salient object detection for searched web images via global saliency. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3194–3201

  48. Xia C, Qi F, Shi G, Wang P (2015) Nonlocal center–surround reconstruction-based bottom-up saliency estimation. Pattern Recog 48(4):1337–1348

    Article  Google Scholar 

  49. Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1155–1162

  50. Yu Z, Wong H S (2007) A rule based technique for extraction of visual attention regions based on real-time clustering. IEEE Trans Multimed 9(4):766–784

    Article  Google Scholar 

  51. Zhai Y, Shah M (2006) Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th ACM international conference on multimedia. ACM, pp 815–824

  52. Zhang L, Yang L, Luo T (2016) Unified saliency detection model using color and texture features. PloS One 11(2):e0149,328

    Article  Google Scholar 

  53. Zhou L, Yang Z, Yuan Q, Zhou Z, Hu D (2015) Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans Image Process 24(11):3308–3320

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mudassir Rafi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rafi, M., Mukhopadhyay, S. Salient object detection employing regional principal color and texture cues. Multimed Tools Appl 78, 19735–19751 (2019). https://doi.org/10.1007/s11042-019-7153-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7153-z

Keywords

Navigation