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Saliency detection based on hybrid artificial bee colony and firefly optimization

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Abstract

Saliency detection is one of the challenging problems still tackled by image processing and computer vision research communities. Although not very numerous, recent studies reveal that optimization-based methods provide relatively accurate and fast solutions for such problems. This paper presents a novel unsupervised hybrid optimization method that aims to propose reasonable solution to saliency detection problem by combining the familiar artificial bee colony and firefly algorithms. The proposed method, HABCFA, is based on creating hybrid-personality individuals behaving like both bees and fireflies. A superpixel-based method is used to obtain better background intensity values in the saliency detection process, providing a better precision in extracting the salient regions. HABCFA algorithm is capable of achieving an optimum saliency map without requiring any extra mask or training step. HABCFA has produced superior performance against its basis algorithms, artificial bee colony, and firefly on four known benchmark problems regarding convergence rate and iteration count. On the other hand, the experimental results on four commonly used datasets, including MSRA-1000, ECSSD, ICOSEG, and DUTOMRON, demonstrate that HABCFA is adequately robust and effective in terms of accuracy, precision, and speed in comparison with the eleven state-of-the-art methods.

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References

  1. Aksac A, Ozyer T, Alhajj R (2017) Complex networks driven salient region detection based on superpixel segmentation. Pattern Recognit 66:268–279. https://doi.org/10.1016/j.patcog.2017.01.010

    Article  Google Scholar 

  2. Rutishauser U, Walther D, Koch C, Perona P (2004) Is bottom-up attention useful for object recognition?. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 37–44. https://doi.org/10.1109/cvpr.2004.1315142

  3. Jiang H, Wang J, Yuan Z, Liu T, Zheng N (2011) Automatic salient object segmentation based on context and shape prior. In: Hoey J, McKenna S, Trucco E (eds) Proceedings of the British Machine Vision Conference. BMVA Press, pp 110.1–110.12. https://doi.org/10.5244/C.25.110

  4. Wang W, Shen J, Yang R, Porikli F (2018) Saliency-aware Video object segmentation. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2017.2662005

    Article  Google Scholar 

  5. Chen HY, Leou JJ (2010) Saliency-directed image interpolation using particle swarm optimization. Signal Process 90:1676–1692. https://doi.org/10.1016/j.sigpro.2009.11.019

    Article  MATH  Google Scholar 

  6. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259. https://doi.org/10.1109/34.730558

    Article  Google Scholar 

  7. Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In Proceedings IEEE Conference on computer vision and pattern recognition, pp 1–8. https://doi.org/10.1109/CVPR.2007.383267

  8. Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. InProceedings of the IEEE conference on computer vision and pattern recognition, pp 1265–1274. https://doi.org/10.1109/CVPR.2015.7298731

  9. Fergus R, Perona P, Zisserman A (2003) Object class recognition by unsupervised scale-invariant learning. InProceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 264–271. https://doi.org/10.1109/cvpr.2003.1211479

  10. Yang J, Yang MH (2017) Top-down visual saliency via joint CRF and dictionary learning. IEEE Trans Pattern Anal Mach Intell 39:576–588. https://doi.org/10.1109/TPAMI.2016.2547384

    Article  Google Scholar 

  11. 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 33:353–367. https://doi.org/10.1109/TPAMI.2010.70

    Article  Google Scholar 

  12. Singh N, Arya R, Agrawal RK (2014) A novel approach to combine features for salient object detection using constrained particle swarm optimization. Pattern Recognit 47:1731–1739. https://doi.org/10.1016/j.patcog.2013.11.012

    Article  Google Scholar 

  13. Chang KY, Liu TL, Chen HT, Lai SH (2011) Fusing generic objectness and visual saliency for salient object detection. InProceedings IEEE International Conference on Computer Vision, pp 914–921. https://doi.org/10.1109/ICCV.2011.6126333

  14. Ma L, Tian J, Yu W (2010) Visual saliency detection in image using ant colony optimisation and local phase coherence. Electron Lett 46:1066–1068. https://doi.org/10.1049/el.2010.0072

    Article  Google Scholar 

  15. Lee CY, Leou JJ, Hsiao HH (2012) Saliency-directed color image segmentation using modified particle swarm optimization. Signal Process 92:1–18. https://doi.org/10.1016/j.sigpro.2011.04.026

    Article  Google Scholar 

  16. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University

  17. Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications, Lecture notes in computer science, vol 5792. Springer, Berlin and Heidelberg, pp 169–178. https://doi.org/10.1007/978-3-642-04944-6_14

    Chapter  Google Scholar 

  18. 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 Pat Anal Mach Intel 34:1–8

    Article  Google Scholar 

  19. Lei Z, Chai W, Zhao S, Song H, Li F (2017) Saliency detection for RGBD image using optimization. ICCSE 2017-12th international conference on computer science and education, Institute of Electrical and Electronics Engineers Inc pp 440–443. https://doi.org/10.1109/ICCSE.2017.8085532

  20. Varghese AA, Rajan JE (2016) A fire fly optimization based video object co-segmentation. Int J Eng Res Sci 2:57–63

    Google Scholar 

  21. Horng MH (2011) Multi-level thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst Appl 38:13785–13791. https://doi.org/10.1016/j.eswa.2011.04.180

    Article  Google Scholar 

  22. Hancer E (2019) Artificial bee colony: theory, literature review, and application in image segmentation. Recent Adv Memet Algorithms Appl Image Process. https://doi.org/10.1007/978-981-15-1362-6_3

    Article  Google Scholar 

  23. Gao H, Fu Z, Pun C, Hu H, Lan R (2018) A multi-level thresholding image segmentation based on an improved artificial bee colony algorithm. Comput Electr Eng 70:931–938. https://doi.org/10.1016/j.compeleceng.2017.12.037

    Article  Google Scholar 

  24. Yiğitbaşı E, Baykan N (2013) Edge detection using artificial bee colony algorithm (ABC). Int J Inf Electron Eng 3:634–638. https://doi.org/10.7763/IJIEE.2013.V3.394

    Article  Google Scholar 

  25. Chakrabarty A, Jain H, Chatterjee A (2013) Volterra kernel based face recognition using artificial bee colony optimization. Eng Appl Artif Intell 26:1107–1114. https://doi.org/10.1016/j.engappai.2012.09.015

    Article  Google Scholar 

  26. He L, Huang S (2017) Modified firefly algorithm based multi-level thresholding for color image segmentation. Neurocomputing 240:152–174. https://doi.org/10.1016/j.neucom.2017.02.040

    Article  Google Scholar 

  27. Rajinikanth V, Couceiro MS (2015) RGB histogram based color image segmentation using firefly algorithm. Proced Comput Sci 46:1449–1457. https://doi.org/10.1016/j.procs.2015.02.064

    Article  Google Scholar 

  28. Zhang L, Mistry K, Neoh SC, Lim CP (2016) Intelligent facial emotion recognition using moth-firefly optimization. Knowl-Based Syst 111:248–267. https://doi.org/10.1016/j.knosys.2016.08.018

    Article  Google Scholar 

  29. Schumer MA, Steiglitz K (1968) Adaptive step size random search. IEEE Trans Autom Control 13:270–276. https://doi.org/10.1109/TAC.1968.1098903

    Article  Google Scholar 

  30. Ackley DH (1987) A connectionist machine for genetic hillclimbing. Kluwer Academic Publishers, USA

    Book  Google Scholar 

  31. Fletcher R, Powell MJD (1963) A rapidly convergent descent method for minimization. Comput J 62:163–168. https://doi.org/10.1093/comjnl/6.2.163

    Article  MathSciNet  MATH  Google Scholar 

  32. Schwefel HP (1981) Numerical optimization for computer models. John Wiley Sons, New Jersey

    MATH  Google Scholar 

  33. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. InProceedings IEEE conference on computer vision and pattern recognition, Institute of Electrical and Electronics Engineers (IEEE) pp 1597–1604. https://doi.org/10.1109/cvpr.2009.5206596

  34. Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. InProceedings of the IEEE conference on computer vision and pattern recognition. pp 1155–1162. https://doi.org/10.1109/CVPR.2013.153

  35. Batra D, Kowdle A, Parikh D, Luo J, Chen T (2010) iCoseg: interactive co-segmentation with intelligent scribble guidance. InProceedings of IEEE computer society conference on computer vision and pattern recognition. pp 3169–3176. https://doi.org/10.1109/CVPR.2010.5540080

  36. Yang C, Zhang L, Lu H, Ruan X, Yang MH (2013) Saliency detection via graph-based manifold ranking. In Proceedings of IEEE computer society conference on computer vision and pattern recognition. pp 3166–3173. https://doi.org/10.1109/CVPR.2013.407

  37. Tavakoli HR, Rahtu E, Heikkilä J (2011) Fast and efficient saliency detection using sparse sampling and kernel density estimation. SCIA. https://doi.org/10.1007/978-3-642-21227-7_62

    Article  Google Scholar 

  38. Tu WC, He S, Yang Q, Chien SY (2016) Real-time salient object detection with a minimum spanning tree. In Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE computer society pp 2334–2342. https://doi.org/10.1109/CVPR.2016.256

  39. Nezamabadi-Pour H, Saryazdi S, Rashedi E (2006) Edge detection using ant algorithms. Soft Comput 10:623–628. https://doi.org/10.1007/s00500-005-0511-y

    Article  Google Scholar 

  40. Jiang B, Zhang L, Lu H, Yang C, Yang MH (2013) Saliency detection via absorbing Markov chain. In Proceedings of the IEEE conference on computer vision, Institute of Electrical and Electronics Engineers Inc. pp 1665–1672. https://doi.org/10.1109/ICCV.2013.209

  41. Goferman S, Zelnik-Manor L, Tal A (2012) Context-aware saliency detection. IEEE Trans Pattern Anal Mach Intell 34:1915–1926. https://doi.org/10.1109/TPAMI.2011.272

    Article  Google Scholar 

  42. Rahtu E, Kannala J, Salo M, Heikkilä J (2010) Segmenting salient objects from images and videos. ECCV

  43. Lu H, Li X, Zhang L, Ruan X, Yang MH (2016) Dense and sparse reconstruction error based saliency descriptor. IEEE Trans Image Process 25:1592–1603. https://doi.org/10.1109/TIP.2016.2524198

    Article  MathSciNet  MATH  Google Scholar 

  44. Seo HJ, Milanfar P (2009) Static and space-time visual saliency detection by self-resemblance. J Vis 9:1–27. https://doi.org/10.1167/9.12.15

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technologies, Faculty of Arts and Sciences, FMV Işık University, Istanbul, Turkey

    Elif Deniz Yelmenoglu

  2. Department of Information System Engineering, Faculty of Computer and Information Sciences, Sakarya University, Sakarya, Turkey

    Numan Celebi & Tugrul Tasci

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  1. Elif Deniz Yelmenoglu
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  3. Tugrul Tasci
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Correspondence to Elif Deniz Yelmenoglu.

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Yelmenoglu, E.D., Celebi, N. & Tasci, T. Saliency detection based on hybrid artificial bee colony and firefly optimization. Pattern Anal Applic 25, 757–772 (2022). https://doi.org/10.1007/s10044-022-01063-6

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