1. | Aminul Islam; Sk Md Masudul Ahsan; Joo Kooi Tan Saliency detection using the combination of boundary aware color-map and seam-map Conference 5th IEEE Intl. Conf. on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Bangladesh, 2019, ISBN: 978-1-7281-3060-6. Abstract | Links | BibTeX @conference{Islam2019,
title = {Saliency detection using the combination of boundary aware color-map and seam-map},
author = {Aminul Islam and Sk Md Masudul Ahsan and Joo Kooi Tan},
doi = {https://doi.org/10.1109/CIET.2018.8660825},
isbn = {978-1-7281-3060-6},
year = {2019},
date = {2019-07-15},
booktitle = {5th IEEE Intl. Conf. on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Bangladesh},
abstract = {Salient region of an image is usually detected by using contrast and boundary priors. Along with those cues the use of seam importance map has shown promising output previously. In this study, better result is found by further exploiting the seam-map using spatial distance and color information in combination with boundary prior. Color and seam maps are also down-weighted using average spatial distance to other regions. Moreover, passing the superpixelized version of the input image into seam and color map generation procedure has improved the output. Experimental results based on MSRA 1k dataset are presented with ten state of the art methods. F-beta measures are presented along with precision recall curves to better understand the outcome. The performance comparison with compared researches proofs superiority of the proposed method.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Salient region of an image is usually detected by using contrast and boundary priors. Along with those cues the use of seam importance map has shown promising output previously. In this study, better result is found by further exploiting the seam-map using spatial distance and color information in combination with boundary prior. Color and seam maps are also down-weighted using average spatial distance to other regions. Moreover, passing the superpixelized version of the input image into seam and color map generation procedure has improved the output. Experimental results based on MSRA 1k dataset are presented with ten state of the art methods. F-beta measures are presented along with precision recall curves to better understand the outcome. The performance comparison with compared researches proofs superiority of the proposed method. |
2. | Aminul Islam; Sk Md Masudul Ahsan; Joo Kooi Tan Saliency detection using boundary aware regional contrast based seam-map Conference IEEE International Conference on Innovation in Engineering and Technology (ICIET), Bangladesh, 2018, ISBN: 978-1-5386-5229-9. Abstract | Links | BibTeX @conference{Islam2018,
title = {Saliency detection using boundary aware regional contrast based seam-map},
author = {Aminul Islam and Sk Md Masudul Ahsan and Joo Kooi Tan},
url = {https://doi.org/10.1109/CIET.2018.8660825},
isbn = {978-1-5386-5229-9},
year = {2018},
date = {2018-12-28},
booktitle = {IEEE International Conference on Innovation in Engineering and Technology (ICIET), Bangladesh},
abstract = {Most of the saliency detection methods use the contrast and boundary priors to extract the salient region of an input image. These two approaches are followed in Boundary Aware Regional Contrast Based Visual Saliency Detection (BARC) along with spatial distance information to achieve state of the art result. In this research, a more interesting cue is introduced to extract the salient region from an input image. Here, a combination of seam map and BARC is presented to produce the saliency output. Seam importance map with boundary prior is also presented to measure the performance of this combination. Experiments with ten state of the art methods reveal that we get better saliency output by combining seam information of an input image with BARC.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Most of the saliency detection methods use the contrast and boundary priors to extract the salient region of an input image. These two approaches are followed in Boundary Aware Regional Contrast Based Visual Saliency Detection (BARC) along with spatial distance information to achieve state of the art result. In this research, a more interesting cue is introduced to extract the salient region from an input image. Here, a combination of seam map and BARC is presented to produce the saliency output. Seam importance map with boundary prior is also presented to measure the performance of this combination. Experiments with ten state of the art methods reveal that we get better saliency output by combining seam information of an input image with BARC. |
3. | Sk Md Masudul Ahsan; Joo Kooi Tan; Hyoungseop Kim; Seiji Ishikawa Boundary aware regional contrast based visual saliency detection (BARC) Conference 21st Intl. Symposium on Artificial Life and Robotics (ISAROB '16), Japan, 2016. BibTeX @conference{ahsan2016,
title = {Boundary aware regional contrast based visual saliency detection (BARC)},
author = {Sk Md Masudul Ahsan and Joo Kooi Tan and Hyoungseop Kim and Seiji Ishikawa},
year = {2016},
date = {2016-07-01},
booktitle = {21st Intl. Symposium on Artificial Life and Robotics (ISAROB '16)},
address = {Japan},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
4. | S. Goferman; L. Zelnik-Manor; A. Tal Context-Aware Saliency Detection Conference IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012. BibTeX @conference{Goferman:2012,
title = {Context-Aware Saliency Detection},
author = {S. Goferman and L. Zelnik-Manor and A. Tal},
year = {2012},
date = {2012-07-05},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
5. | M. Cheng; G. Zhang; N. J. Mitra; X. Huang; S. Hu Global contrast based salient region detection Conference Proc. CVPR, 2011. BibTeX @conference{Cheng:2011,
title = {Global contrast based salient region detection},
author = {M. Cheng and G. Zhang and N. J. Mitra and X. Huang and S. Hu},
year = {2011},
date = {2011-07-01},
booktitle = {Proc. CVPR},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
6. | L. Duan; C. Wu; J. Miao; L. Qing; Y. Fu Visual saliency detection by spatially weighted dissimilarity Conference Proc. CVPR, 2011. BibTeX @conference{Duan2011,
title = {Visual saliency detection by spatially weighted dissimilarity},
author = {L. Duan and C. Wu and J. Miao and L. Qing and Y. Fu},
year = {2011},
date = {2011-07-01},
booktitle = {Proc. CVPR},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
7. | R. Achanta; S. Süsstrunk Saliency detection using maximum symmetric surround Conference Proc. IEEE Int. Conf. Image Processing, 2010. BibTeX @conference{Achanta2010,
title = {Saliency detection using maximum symmetric surround},
author = {R. Achanta and S. Süsstrunk},
year = {2010},
date = {2010-07-01},
booktitle = {Proc. IEEE Int. Conf. Image Processing},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
8. | R. Achanta; S. Hemami; F. Estrada; S. Susstrunk Frequency-tuned salient region detection Conference Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009. BibTeX @conference{Achanta:2009,
title = {Frequency-tuned salient region detection},
author = {R. Achanta and S. Hemami and F. Estrada and S. Susstrunk},
year = {2009},
date = {2009-08-01},
booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
9. | N. D. B. Bruce; J. K. Tsotsos Saliency, attention, and visual search: An information theoretic approach Journal Article In: Journal of Vision, 2009. BibTeX @article{Bruce2009,
title = {Saliency, attention, and visual search: An information theoretic approach},
author = {N. D. B. Bruce and J. K. Tsotsos},
year = {2009},
date = {2009-07-09},
journal = {Journal of Vision},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
10. | Bernhard Schölkopf; John Platt; Thomas Hofmann Graph-Based Visual Saliency Conference 2007. BibTeX @conference{Schoelkopf2007,
title = {Graph-Based Visual Saliency},
author = {Bernhard Schölkopf and John Platt and Thomas Hofmann},
year = {2007},
date = {2007-08-02},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
11. | Zhai, Yun; Shah, Mubarak Visual Attention Detection in Video Sequences Using Spatiotemporal Cues Conference Proc. of the 14th ACM Int. Conf. on Multimedia, 2006. BibTeX @conference{Zhai2006,
title = {Visual Attention Detection in Video Sequences Using Spatiotemporal Cues},
author = {Zhai, Yun and Shah, Mubarak},
year = {2006},
date = {2006-07-01},
booktitle = {Proc. of the 14th ACM Int. Conf. on Multimedia},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|