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A Novel Saliency Measure Using Entropy and Rule of Thirds

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Machine Intelligence and Signal Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 748))

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Abstract

Human intelligence can easily identify the visually attractive object, i.e., salient object with high accuracy in real time. It is an issue of concern to design an efficient computational model which can imitate human behavior such that the model attains better detection accuracy and takes less computation time. Until now, many models have been designed which are either better regarding detection accuracy or computation time but not both. This paper aims to realize a model that takes less computational time and at the same time attains higher detection accuracy. In this work, we propose a novel saliency detection model via the efficient use of entropy and boost the performance of saliency detection by employing the concept of the rule of thirds. The paper compares the performance of the proposed model with eighteen existing models on six publicly available datasets. With regard to precision, recall, and F-measure on all the six datasets, experimental results indicate better performance of the proposed model. Less computation time required by the proposed model in comparison to many state-of-the-art models.

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References

  1. Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a survey (2014). arXiv preprint arXiv:1411.5878

  2. Itti, L.: Models of bottom-up and top-down visual attention. Doctoral Dissertation, California Institute of Technology (2000)

    Google Scholar 

  3. Li, Z., Itti, L.: Saliency and gist features for target detection in satellite images. IEEE Trans. Image Process. 20(7), 2017–2029 (2011)

    Article  MathSciNet  Google Scholar 

  4. Chen, L.Q., Xie, X., Fan, X., Ma, W.Y., Zhang, H.J., Zhou, H.Q.: A visual attention model for adapting images on small displays. Multimed. syst. 9(4), 353–364 (2003)

    Article  Google Scholar 

  5. Santella, A., Agrawala, M., DeCarlo, D., Salesin, D., Cohen, M.: Gaze-based interaction for semi-automatic photo cropping. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 771–780. ACM, USA (2006)

    Google Scholar 

  6. Amit, Y.: 2D target detection and recognition, models, algorithms and networks (2002)

    Google Scholar 

  7. Gonzalez, R.C., Woods R.E.: Digital image processing (2002)

    Google Scholar 

  8. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  9. Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2189–2202 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Advances in Neural Information Processing Systems, pp. 155–162 (2006)

    Google Scholar 

  12. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In Advances in Neural Information Processing Systems, pp. 545–552 (2007)

    Google Scholar 

  13. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07), pp. 1–8. IEEE, USA (2007)

    Google Scholar 

  14. Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: SUN: a Bayesian framework for saliency using natural statistics. J. Vis. 8(7), 32–32 (2008)

    Article  Google Scholar 

  15. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009). pp. 1597–1604. IEEE, USA (2009)

    Google Scholar 

  16. Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2653–2656. IEEE, USA (2010)

    Google Scholar 

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

    Article  Google Scholar 

  18. Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 853-860. IEEE, USA (2012)

    Google Scholar 

  19. Vikram, T.N., Tscherepanow, M., Wrede, B.: A saliency map based on sampling an image into random rectangular regions of interest. Pattern Recognit. 45(9), 3114–3124 (2012)

    Article  Google Scholar 

  20. Imamoglu, N., Lin, W., Fang, Y.: A saliency detection model using low-level features based on wavelet transform. IEEE Trans. Multimed. 15(1), 96–105 (2013)

    Article  Google Scholar 

  21. Xie, Y., Lu, H., Yang, M.H.: Bayesian saliency via low and mid level cues. IEEE Trans. Image Process. 22(5), 1689–1698 (2013)

    Article  MathSciNet  Google Scholar 

  22. Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M. H.: Saliency detection via absorbing markov chain. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1665–1672 (2013)

    Google Scholar 

  23. Fu, H., Cao, X., Tu, Z.: Cluster-based co-saliency detection. IEEE Trans. Image Process. 22(10), 3766–3778 (2013)

    Article  MathSciNet  Google Scholar 

  24. Singh, N., Arya, R., Agrawal, R.K.: A novel approach to combine features for salient object detection using constrained particle swarm optimization. Pattern Recognit. 47(4), 1731–1739 (2014)

    Article  Google Scholar 

  25. Liu, Z., Zou, W., Le Meur, O.: Saliency tree: a novel saliency detection framework. IEEE Trans. Image Process. 23(5), 1937–1952 (2014)

    Article  MathSciNet  Google Scholar 

  26. Zhu, L., Klein, D.A., Frintrop, S., Cao, Z., Cremers, A.B.: A multisize superpixel approach for salient object detection based on multivariate normal distribution estimation. IEEE Trans. Image Process. 23(12), 5094–5107 (2014)

    Article  MathSciNet  Google Scholar 

  27. Singh, N., Agrawal, R.K.: Combination of Kullback-Leibler divergence and Manhattan distance measures to detect salient objects. Signal Image Video Process. 9(2), 427–435 (2015)

    Article  Google Scholar 

  28. Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 110–119 (2015)

    Google Scholar 

  29. Singh, N., Arya, R., Agrawal, R.K.: A novel position prior using fusion of rule of thirds and image center for salient object detection. Multimed. Tools Appl. 76(8), 10521–10538 (2017)

    Article  Google Scholar 

  30. Singh, N., Arya, R., Agrawal, R.K.: A convex hull approach in conjunction with Gaussian mixture model for salient object detection. Digit. Signal Process. 55, 22–31 (2016)

    Article  Google Scholar 

  31. Singh, N., Arya, R., Agrawal, R. K.: Performance enhancement of salient object detection using superpixel based Gaussian mixture model. Multimed. Tools Appl. pp. 1–19 (2017)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, NIT Uttarakhand, Srinagar (Garhwal), India

    Priyanka Bhatt & Navjot Singh

Authors
  1. Priyanka Bhatt
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  2. Navjot Singh
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Corresponding author

Correspondence to Priyanka Bhatt .

Editor information

Editors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    Ram Bilas Pachori

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Bhatt, P., Singh, N. (2019). A Novel Saliency Measure Using Entropy and Rule of Thirds. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_40

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