skip to main content
10.1145/2816839.2816883acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiciipConference Proceedingsconference-collections
research-article

A Novel Saliency Detection Model based on Self Organizing Tree Algorithm

Published: 23 November 2015 Publication History

Abstract

Image saliency detection is very useful in many computer vision tasks while it still remains a challenging problem. In this paper, we propose a novel bottom-up model for visual saliency detection. The proposed model is based on both unsupervised learning approach and frequency domain analysis. Firstly, we incorporate the influence of center bias into our model, which is a common phenomenon that directs visual attention to the center of images in natural scenes. Hence, we introduce an unsupervised neural network that aims to measure the saliency center bias by exploring both color and texture low level cues. Secondly, the proposed model integrates Log-Gabor wavelets for visual saliency detection. This choice is justified by the fact that i) human visual system behavior detection of salient objects in a visual scene can be well modeled by band-pass filtering, ii) compared with the traditional model of receptive field, the Log-Gabor wavelets can better simulate the biological characteristics of the simple cortical cell in the receptive filed. Quantitative and qualitative experimental evaluation on MSRA-1000 public image dataset depicts the promising results from the proposed model by outperforming the relevant state of the art saliency detection models.

References

[1]
Achanta, R., Estrada, F., Wils, P., and Süsstrunk, S. 2008. Salient region detection and segmentation. Proceedings of the 6th international conference on Computer vision (ICVS'08). 66--75. May 12-15, 2008. Santorini, Greece.
[2]
Achanta, R., Hemami, S., Estrada, F. J., & Susstrunk, S. 2009. Frequency-tuned salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09). 1597--1604. (Miami, FL June 20-25, 2009) DOI= http://dx.doi.org/10.1109/CVPR.2009.5206596
[3]
Achanta, R., Susstrunk S. 2010. Saliency detection using maximum symmetric surround. In IEEE International Conference on Image Processing (ICIP). 2653--2656.
[4]
Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu, S. 2011. Global contrast based salient region detection. CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 409--416
[5]
Dopazo, J., Carazo, J. M. 1997. Phylogenetic reconstruction using and unsupervised growing neural network that adopts the topology of a phylogenetic tree. Journal of Molecular Evolution, 44 (2), 226--233.
[6]
Fields, D. 1987. Relations between the statistics of natural image and the response properties of cortical cells. Journal of Optical Society of America, 4(12), 2379--2394.
[7]
Frintrop, S., Klodt, M. and Rome, E. 2007. A real-time visual attention system using integral images. In International Conference on Computer Vision Systems (ICVS).
[8]
Fu, H., Cao, X., and Tu, Z., 2013. Cluster-based Co-saliency Detection. In IEEE Transactions on Image Processing (TIP), 22(10), 3766--3778.
[9]
Goferman, S., Zelnik-Manor, L., Tal, A. 2010. Context-aware saliency detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10]
Harel J., Koch C., Perona P. 2007. Graph-based visual saliency, In Advances in Neural Information Processing Systems 19 (NIPS).
[11]
Herrero, J., Valencia, A., J. Dopazo. 2001. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Journal of Bioinformatics, 17(2), 126--136.
[12]
Hou, X., Zhang, L. 2007. Saliency detection: a spectral residual approach. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1-8. November 6, 2007.
[13]
Hou, X., Harel, J., Koch, C. 2012. Image signature: highlighting sparse salient regions, In IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI), 34(1), 194--201.
[14]
Hu, Y., Xie, X., Ma, W. Y., Chia, L.-T., Rajan, D. 2004. Salient region detection using weighted feature maps based on the human visual attention model. Springer Lecture Notes in Computer Science, 3332(2), 993--1000.
[15]
İmamoğlu, N., Lin, W. 2013. A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform, IEEE Transactions on Multimedia, 15(1).
[16]
Itti, L., Koch, C., and Niebur, E. 1998. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 20(11), 1254--1259, November 1998. DOI= http://dx.doi.org/10.1109/34.730558
[17]
Itti, L. and Koch, C. 1999. Comparison of feature combination strategies for saliency-based visual attention systems. In SPIE Human Vision and Electronic Imaging IV (HVEI), pp. 473--482.
[18]
Itti, L., Borji, A. 2013. Computational Models: Bottom-up and Top-down Aspects, In: The Oxford Handbook of Attention, (A. C. Nobre, S. Kastner Ed.), pp. 1--20. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19]
Lee, T. S. 1996. Image representation using 2D Gabor wavelets, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18 (10), 959--971.
[20]
Liu, T., Sun, J., Zheng, N. N., Tang, X., Shum, H. Y. 2007. Learning to Detect A Salient Object. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21]
Ma, Y. F., Zhang, H. J. 2003. Contrast-based image attention analysis by using fuzzy growing. In ACM International Conference on Multimedia.
[22]
Navalpakkam, V., Itti, L. 2006. An integrated model of top-down and bottom-up attention for optimizing detection speed. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23]
Rahtu, E., Kannala, J., Salo, M., Heikkil, J. 2010. Segmenting salient objects from images and videos. In European Conference on Computer Vision (ECCV).
[24]
Rutishauser, U., Walther, D., Koch, C., Perona, P. 2004. Is bottom-up attention useful for object recognition? In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25]
Walther, D., Itti, L., Riesenhuber, M., Poggio, T., Koch, C. 2002. Attentional selection for object recognition - a gentle way In Biologically Motivated Computer Vision, Second IEEE International Workshop (BMCV).
[26]
Walther, D. and Koch, C. 2006. Modeling attention to salient proto-objects. Journal of Neural Networks, 19(9), 1395--1407.
[27]
Xue, Y., Liu, Z., Shi, R. 2011. Saliency detection using multiple region-based features, Journal of Optical Engineering, 50(5).
[28]
Yang, W., Tang, Y. Y., Fang, B., Shang, Z. and Lin, Y. 2013. Visual saliency detection with center shift. Journal of Neurocomputing, 103, 63--74.
[29]
Zhai, Y., Shah, M. 2006. Visual attention detection in video sequences using spatiotemporal cues. In ACM Multimedia, 815--824
[30]
Zhang, L., Gu, Z., and Li, H. 2013. SDSP: A novel saliency detection method by combining simple priors. In IEEE International Conference on Image Processing (ICIP).
  1. A Novel Saliency Detection Model based on Self Organizing Tree Algorithm

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    IPAC '15: Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication
    November 2015
    495 pages
    ISBN:9781450334587
    DOI:10.1145/2816839
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 23 November 2015

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Gabor Wavelet
    2. Neural Network
    3. Saliency detection
    4. saliency map
    5. saliency model

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    IPAC '15

    Acceptance Rates

    Overall Acceptance Rate 87 of 367 submissions, 24%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 79
      Total Downloads
    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 14 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media