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Unconstrained Salient Object Detection

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Visual Saliency: From Pixel-Level to Object-Level Analysis

Abstract

In this chapter, we aim at detecting generic salient objects in unconstrained images, which may contain multiple salient objects or no salient object. Solving this problem entails generating a compact set of detection windows that matches the number and the locations of salient objects.

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Notes

  1. 1.

    http://www.cs.bu.edu/groups/ivc/SOD/.

  2. 2.

    Rank thresholding means outputting a fixed number of proposals for each image, which is a default setting for object proposal methods like SS, EB, and MCG, as their proposal scores are less calibrated across images.

References

  1. Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. Frequency-tuned salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009).

    Google Scholar 

  2. Alexe, B., Deselaers, T., and Ferrari, V. Measuring the objectness of image windows. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 34, 11 (2012), 2189–2202.

    Article  Google Scholar 

  3. Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., and Malik, J. Multiscale combinatorial grouping. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014).

    Google Scholar 

  4. Ba, J., Mnih, V., and Kavukcuoglu, K. Multiple object recognition with visual attention. In International Conference on Learning Representations (ICLR) (2015).

    Google Scholar 

  5. Barinova, O., Lempitsky, V., and Kholi, P. On detection of multiple object instances using Hough transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 34, 9 (2012), 1773–1784.

    Article  Google Scholar 

  6. Borji, A., Cheng, M.-M., Jiang, H., and Li, J. Salient object detection: A benchmark. IEEE Transactions on Image Processing (TIP) 24, 12 (2015), 5706–5722.

    Article  MathSciNet  Google Scholar 

  7. Buchbinder, N., Feldman, M., Naor, J., and Schwartz, R. A tight linear time (1/2)-approximation for unconstrained submodular maximization. In Foundations of Computer Science (2012).

    Google Scholar 

  8. Carreira, J., and Sminchisescu, C. CPMC: Automatic object segmentation using constrained parametric min-cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 34, 7 (2012), 1312–1328.

    Article  Google Scholar 

  9. Chang, K.-Y., Liu, T.-L., Chen, H.-T., and Lai, S.-H. Fusing generic objectness and visual saliency for salient object detection. In IEEE International Conference on Computer Vision (ICCV) (2011).

    Google Scholar 

  10. Chen, X., and Gupta, A. Webly supervised learning of convolutional networks. In IEEE International Conference on Computer Vision (ICCV) (2015).

    Google Scholar 

  11. Cheng, M.-M., Mitra, N. J., Huang, X., Torr, P. H. S., and Hu, S.-M. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 37, 3 (2015), 569–582.

    Article  Google Scholar 

  12. Cheng, M.-M., Zhang, Z., Lin, W.-Y., and Torr, P. H. S. BING: Binarized normed gradients for objectness estimation at 300fps. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014).

    Google Scholar 

  13. Dalal, N., and Triggs, B. Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2005).

    Google Scholar 

  14. Desai, C., Ramanan, D., and Fowlkes, C. C. Discriminative models for multi-class object layout. International Journal of Computer Vision (IJCV) 95, 1 (2011), 1–12.

    MathSciNet  MATH  Google Scholar 

  15. Deselaers, T., Alexe, B., and Ferrari, V. Weakly supervised localization and learning with generic knowledge. International Journal of Computer Vision (IJCV) 100, 3 (2012), 275–293.

    Article  MathSciNet  Google Scholar 

  16. Desimone, R., and Duncan, J. Neural mechanisms of selective visual attention. Annual review of neuroscience 18, 1 (1995), 193–222.

    Article  Google Scholar 

  17. Erhan, D., Szegedy, C., Toshev, A., and Anguelov, D. Scalable object detection using deep neural networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014).

    Google Scholar 

  18. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html.

  19. Feige, U., Mirrokni, V. S., and Vondrak, J. Maximizing non-monotone submodular functions. SIAM Journal on Computing 40, 4 (2011), 1133–1153.

    Article  MathSciNet  Google Scholar 

  20. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 32, 9 (2010), 1627–1645.

    Article  Google Scholar 

  21. Feng, J., Wei, Y., Tao, L., Zhang, C., and Sun, J. Salient object detection by composition. In IEEE International Conference on Computer Vision (ICCV) (2011).

    Google Scholar 

  22. Girshick, R., Donahue, J., Darrell, T., and Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014), IEEE.

    Google Scholar 

  23. Gopalakrishnan, V., Hu, Y., and Rajan, D. Random walks on graphs to model saliency in images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009).

    Google Scholar 

  24. Hosang, J., Benenson, R., and Schiele, B. How good are detection proposals, really? In BMVC (2014).

    Google Scholar 

  25. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. Caffe: Convolutional architecture for fast feature embedding. In ACM International Conference on Multimedia (2014).

    Google Scholar 

  26. Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., and Li, S. Salient object detection: A discriminative regional feature integration approach. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013).

    Google Scholar 

  27. Karpathy, A., and Fei-Fei, L. Deep visual-semantic alignments for generating image descriptions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015).

    Google Scholar 

  28. Larochelle, H., and Hinton, G. E. Learning to combine foveal glimpses with a third-order Boltzmann machine. In Advances in Neural Information Processing Systems (NIPS) (2010).

    Google Scholar 

  29. Li, Y., Hou, X., Koch, C., Rehg, J. M., and Yuille, A. L. The secrets of salient object segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014).

    Google Scholar 

  30. Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., and Shum, H.-Y. Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 33, 2 (2011), 353–367.

    Google Scholar 

  31. Lu, S., Mahadevan, V., and Vasconcelos, N. Learning optimal seeds for diffusion-based salient object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014).

    Google Scholar 

  32. Lu, Y., Zhang, W., Lu, H., and Xue, X. Salient object detection using concavity context. In IEEE International Conference on Computer Vision (ICCV) (2011).

    Google Scholar 

  33. Luo, Y., Yuan, J., Xue, P., and Tian, Q. Saliency density maximization for object detection and localization. In Asian Conference on Computer Vision (ACCV) (2011).

    Chapter  Google Scholar 

  34. Mairon, R., and Ben-Shahar, O. A closer look at context: From coxels to the contextual emergence of object saliency. In European Conference on Computer Vision (ECCV) (2014).

    Google Scholar 

  35. Marchesotti, L., Cifarelli, C., and Csurka, G. A framework for visual saliency detection with applications to image thumbnailing. In IEEE International Conference on Computer Vision (ICCV) (2009).

    Google Scholar 

  36. Mnih, V., Heess, N., Graves, A., et al. Recurrent models of visual attention. In Advances in Neural Information Processing Systems (NIPS) (2014).

    Google Scholar 

  37. Ren, S., He, K., Girshick, R., and Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (NIPS) (2015).

    Google Scholar 

  38. Rothe, R., Guillaumin, M., and Van Gool, L. Non-maximum suppression for object detection by passing messages between windows. In ACCV (2014).

    Google Scholar 

  39. Rujikietgumjorn, S., and Collins, R. T. Optimized pedestrian detection for multiple and occluded people. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013), IEEE.

    Google Scholar 

  40. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. Imagenet large scale visual recognition challenge. arXiv preprint arXiv:1409.0575 (2014).

    Google Scholar 

  41. Scharfenberger, C., Waslander, S. L., Zelek, J. S., and Clausi, D. A. Existence detection of objects in images for robot vision using saliency histogram features. In International Conference on Computer and Robot Vision (2013).

    Google Scholar 

  42. Shen, X., and Wu, Y. A unified approach to salient object detection via low rank matrix recovery. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012).

    Google Scholar 

  43. Simonyan, K., and Zisserman, A. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR) (2015).

    Google Scholar 

  44. Siva, P., Russell, C., Xiang, T., and Agapito, L. Looking beyond the image: Unsupervised learning for object saliency and detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013).

    Google Scholar 

  45. Siva, P., and Xiang, T. Weakly supervised object detector learning with model drift detection. In IEEE International Conference on Computer Vision (ICCV) (2011), IEEE, pp. 343–350.

    Google Scholar 

  46. Suh, B., Ling, H., Bederson, B. B., and Jacobs, D. W. Automatic thumbnail cropping and its effectiveness. In ACM symposium on User interface software and technology (2003).

    Google Scholar 

  47. Szegedy, C., Reed, S., Erhan, D., and Anguelov, D. Scalable, high-quality object detection. arXiv preprint arXiv:1412.1441 (2014).

    Google Scholar 

  48. Uijlings, J. R., van de Sande, K. E., Gevers, T., and Smeulders, A. W. Selective search for object recognition. International Journal of Computer Vision (IJCV) 104, 2 (2013), 154–171.

    Article  Google Scholar 

  49. Valenti, R., Sebe, N., and Gevers, T. Image saliency by isocentric curvedness and color. In IEEE International Conference on Computer Vision (ICCV) (2009).

    Google Scholar 

  50. Wang, P., Wang, J., Zeng, G., Feng, J., Zha, H., and Li, S. Salient object detection for searched web images via global saliency. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012).

    Google Scholar 

  51. Yan, Q., Xu, L., Shi, J., and Jia, J. Hierarchical saliency detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013).

    Google Scholar 

  52. Yang, C., Zhang, L., Lu, H., Ruan, X., and Yang, M.-H. Saliency detection via graph-based manifold ranking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013).

    Google Scholar 

  53. Yildirim, G., and Süsstrunk, S. FASA: Fast, Accurate, and Size-Aware Salient Object Detection. In ACCV (2014).

    Google Scholar 

  54. Zhang, J., Ma, S., Sameki, M., Sclaroff, S., Betke, M., Lin, Z., Shen, X., Price, B., and Měch, R. Salient object subitizing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015).

    Google Scholar 

  55. Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., and Měch, R. Minimum barrier salient object detection at 80 fps. In IEEE International Conference on Computer Vision(ICCV) (2015).

    Google Scholar 

  56. Zhu, J.-Y., Wu, J., Wei, Y., Chang, E., and Tu, Z. Unsupervised object class discovery via saliency-guided multiple class learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012).

    Google Scholar 

  57. Zitnick, C. L., and Dollár, P. Edge boxes: Locating object proposals from edges. In European Conference on Computer Vision (ECCV) (2014).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Adobe Inc., San Jose, CA, USA

    Jianming Zhang

  2. Centre for Image Analysis, Uppsala University, Uppsala, Uppsala Län, Sweden

    Filip Malmberg

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

Authors
  1. Jianming Zhang
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  2. Filip Malmberg
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  3. Stan Sclaroff
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Zhang, J., Malmberg, F., Sclaroff, S. (2019). Unconstrained Salient Object Detection. In: Visual Saliency: From Pixel-Level to Object-Level Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-04831-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-04831-0_6

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  • Online ISBN: 978-3-030-04831-0

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