Image aesthetics enhancement using composition-based saliency detection
详细信息    查看全文
  • 作者：Handong Zhao (1)
    Jingjing Chen (1)
    Yahong Han (1) (3)
    Xiaochun Cao (2)

    1. School of Computer Science and Technology     ; Tianjin University ; Tianjin ; 300072 ; China
    3. Tianjin Key Laboratory of Cognitive Computing and Application     ; Tianjin University ; Tianjin ; China
    2. State Key Laboratory of Information Security     ; Institute of Information Engineering ; Chinese Academy of Sciences ; Beijing ; 100093 ; China
  • 关键词：Saliency detection ; Saliency segmentation ; Photography composition ; Depth of field ; Realistic blurring
  • 刊名：Multimedia Systems
  • 出版年：2015
  • 出版时间：March 2015
  • 年：2015
  • 卷：21
  • 期：2
  • 页码：159-168
  • 全文大小：1,609 KB
  • 参考文献：1. Achanta, R., Estrada, F.J., Wils, P., S眉sstrunk, S.: Salient region detection and segmentation. In: International conference on computer vision, system, pp. 66鈥?5 (2008)
    2. Achanta, R., Hemami, S.S., Estrada, F.J., S眉sstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1597鈥?604 (2009)
    3. Bae, S., Durand, F.: Defocus magnification. Comput. Graph. Forum 26, 571鈥?79 (2007) CrossRef
    4. Chen, J., Zhao, H., Han, Y., Cao, X.: Visual saliency detection based on photographic composition. In: International conference on internet multimedia computing and service, pp. 13鈥?6 (2013)
    5. Cheng, M.M., Mitra, N.J., Huang, X., Hu, S.M.: Salient shape: group saliency in image collections. Vis. Comput. 30(4), 443鈥?53 (2014)
    6. Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. In: CVPR, pp. 409鈥?16 (2011)
    7. Daly, S.: The visible differences predictor: an algorithm for the assessment of image fidelity. In: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, pp 2鈥?5 (1992)
    8. Das, S., Ahuja, N.: Performance analysis of stereo, vergence, and focus as depth cues for active vision. IEEE. Trans. Pattern Anal. Mach. Intell.17(12), 1213鈥?219 (1995) CrossRef
    9. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying aesthetics in photographic images using a computational approach. In: ECCV, pp. 288鈥?01 (2006)
    10. Datta, R., Li, J., Wang, J.Z.: Learning the consensus on visual quality for next generation image management. In: ACM multimedia, pp. 533鈥?36 (2007)
    11. Datta, R., Li, J., Wang, J.Z.: Algorithmic inferencing of aesthetics and emotion in natural images: An exposition. In: ICIP, special session on image aesthetics: mood and emotion, pp. 105鈥?08 (2008)
    12. Davies, E.R.: Machine vision: theory, algorithms and practicalities. In: pp. 42鈥?4. Academic Press, London (1990)
    13. Eltoukhy, H.A., Kavusi, S.: Computationally efficient algorithm for multifocus image reconstruction. In: Sensors and camera systems for scientific, industrial, and digital photography applications, pp. 332鈥?41 (2003)
    14. Forsyth, D.A., Ponce, J.: Computer vision: a modern approach. Prentice Hall Professional Technical Reference (2002)
    15. Goferman, S., Manor, L.Z., Tal, A.: Context-aware saliency detection. In: CVPR, pp. 2376鈥?383 (2010)
    16. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems (NIPS), pp. 545鈥?52 (2006)
    17. Hong, R., Wang, M., Xu, M., Yan, S., Chua, T.S.: Dynamic captioning: Video accessibility enhancement for hearing impairment. In: ACM multimedia, pp. 421鈥?30 (2010)
    18. Hong, R., Wang, M., Yuan, X.T., Xu, M., Jiang, J., Yan, S., Chua, T.S.: Video accessibility enhancement for hearing impaired users. ACM. Trans. Multimed. Comput.7S, 24鈥?2 (2011)
    19. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1鈥? (2007)
    20. Huhle, B., Schairer, T., Jenke, P., Stra脽er, W.: Realistic depth blur for images with range data. Dynamic 3D, imaging pp. 84鈥?5 (2009)
    21. Krages, B.: Photography: the art of composition, 1st edn. Allworth Press, New York (2005)
    22. Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. Comput. Graph. Forum 29, 469鈥?78 (2010) CrossRef
    23. Liu, T., Sun, J., Zheng, N.N., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: CVPR, pp. 1鈥? (2007)
    24. Ma, Y.F., Zhang, H.: Contrast-based image attention analysis by using fuzzy growing. In: ACM multimedia, pp. 374鈥?81 (2003)
    25. Mahmoud, T.A., Marshall, S.: Threshold decomposition driven adaptive morphological filter for image sharpening. In: VISAPP, pp. 40鈥?5 (2007)
    26. Maki, A., Watanabe, M., Geotensity, C.W.: Combining motion and lighting for 3D surface reconstruction. Int. J. Comput. Vis.48(2), 75鈥?0 (2002) CrossRef
    27. Malik, J., Rosenholtz, R.: Computing local surface orientation and shape from texture for curved surfaces. Int. J. Comput. Vis. 23(2), 149鈥?68 (1997) CrossRef
    28. McGuire, M., Matusik, W., Pfister, H., Hughes, J.F., Durand, F.: Defocus video matting. ACM Trans. Graph. 24(3), 567鈥?76 (2005)
    29. Moutoussis, K., Zeki, S.: A direct demonstration of perceptual asynchrony in vision. In: Proceedings of the Royal Society of London. Series B: Biological Sciences, pp. 393鈥?99 (1997)
    30. Nagai, T., Ikehara, M., Kurematsu, A.: Hmm-based surface reconstruction from single images. Syst. Comput. Jpn. 38(11), 80鈥?9 (2007) CrossRef
    31. Peng, B., Veksler, O.: Parameter selection for graph cut based image segmentation. In: BMVC, pp. 332鈥?41 (2008)
    32. Peters, G.: Aesthetic primitives of images for visualization. In: IEEE international conference on information visualization, pp. 316鈥?25 (2007)
    33. Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph 23, 309鈥?14 (2004) CrossRef
    34. Saxena, A., Chung, S.H., Ng, A.Y.: Learning depth from single monocular images. In: Advances in Neural Information Processing Systems (NIPS) (2005)
    35. Saxena, A., Chung, S.H., Ng, A.Y.: 3-d depth reconstruction from a single still image. Int. J. Comput. Vis. 76, 53鈥?9 (2008) CrossRef
    36. Saxena, A., Sun, M., Ng, A.Y.: Make3d: learning 3d scene structure from a single still image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 824鈥?40 (2009) CrossRef
    37. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47, 1鈥?5 (2002) CrossRef
    38. Schavemaker, J.G.M., Reinders, M.J.T., Gerbrands, J.J., Backer, E.: Image sharpening by morphological filtering. Pattern Recogn. 33(6), 997鈥?012 (2000)
    39. Subbarao, M., Wei, T.C., Surya, G.: Focused image recovery from two defocused images recorded with different camera settings. IEEE Trans. Image Process. 4(12), 1613鈥?628 (1995)
    40. Tatler, B.W.: The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. J. Vis. pp. 1鈥?7 (2007)
    41. Valenti, R., Jaimes, A., Sebe, N.: Sonify your face: Facial expressions for sound generation. In: ACM multimedia, pp. 1363鈥?372 (2010)
    42. Valenti, R., Sebe, N., Gevers, T.: Facial expression recognition: a fully integrated approach. In: International conference on image analysis and processing workshops, pp. 125鈥?30 (2007)
    43. Wang, M., Hong, R., Yuan, X.T., Yan, S., Chua, T.S.: Movie2comics: towards a lively video content presentation. Trans. Multimed.14, 858鈥?70 (2012) CrossRef
    44. Watson, A.B.: Toward a perceptual video quality metric. In: SPIE, pp. 139鈥?47 (1998)
    45. Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: ACM multimedia, pp. 815鈥?24 (2006)
    46. Zhang, M., Zhang, L., Sun, Y., Feng, L., Ma, W.Y.: Auto cropping for digital photographs. In: ICME, pp. 438鈥?41 (2005)
  • 刊物类别：Computer Science
  • 刊物主题：Multimedia Information Systems
    Computer Communication Networks
    Operating Systems
    Data Storage Representation
    Data Encryption
    Computer Graphics
  • 出版者：Springer Berlin / Heidelberg
  • ISSN：1432-1882
文摘
Visual saliency detection and segmentation are widely used in many applications in image processing and computer vision. However, existing saliency detection methods have not fully taken the spatial information of salient regions into account. Inspired by the basic photographic composition rules, we present a novel saliency detection method, which utilizes the knowledge of photographic composition as priors to improve the saliency detection results. Moreover, an online parameter selection method is proposed when utilizing GrabCut to achieve the saliency segmentation result. Besides, to test the applicability of our method, we present a novel post-processing framework for the photographs to be more artistic. The salient region and depth map are firstly computed. The salient region keeps its sharpness, while other parts in the photograph get blurred based on the depth map. To our best knowledge, this is a novel image-based attempt to enhance aesthetics by post-processing a photograph via realistic blurring. We test our method on the 1,000 benchmark test images and dataset MSRA. Extensive experimental results show the applicability and effectiveness of our method.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址：北京市海淀区学院路29号 邮编：100083

电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700