Sk Md Masudul Ahsan; Aminul Islam Visual Saliency Detection using Seam and Color Cues Journal Article Advances in Science, Technology and Engineering Systems Journal, 6 (2), pp. 139-153, 2021, ISSN: 2415-6698. Abstract | Links | BibTeX @article{Aminul2021,
title = {Visual Saliency Detection using Seam and Color Cues},
author = {Sk Md Masudul Ahsan and Aminul Islam},
editor = {Prof. Passerini Kazmerski},
url = {https://astesj.com/?smd_process_download=1&download_id=26458},
doi = {10.25046/aj060217},
issn = {2415-6698},
year = {2021},
date = {2021-03-10},
journal = {Advances in Science, Technology and Engineering Systems Journal},
volume = {6},
number = {2},
pages = {139-153},
abstract = {Human have the god gifted ability to focus on the essential part of a visual scenery irrespective of its background. This important area is called the salient region of an image. Computationally achieving this natural human quality is an attractive goal of today’s scientific world. Saliency detection is the technique of finding the salient region of a digital image. The color contrast between the foreground and background present in an image is usually used to extract this region. Seam Map is computed from the cumulative sum of energy values of an image. The proposed method uses seam importance map along with the weighted average of various color channels of Lab color space namely boundary aware color map to extract the saliency map. These two maps are combined and further optimized to get the final saliency output using the optimization technique proposed in a previous study. Some intermediate combinations which are closer to the proposed optimized version but differ in the optimization technique are also presented in this paper. Several standard benchmark datasets including the famous MSRA 10k dataset are used to evaluate performance of the suggested procedure. Precision-recall curve and F-beta values found from the experiments on those datasets and comparison with other state of the art techniques prove the superiority of the proposed method.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human have the god gifted ability to focus on the essential part of a visual scenery irrespective of its background. This important area is called the salient region of an image. Computationally achieving this natural human quality is an attractive goal of today’s scientific world. Saliency detection is the technique of finding the salient region of a digital image. The color contrast between the foreground and background present in an image is usually used to extract this region. Seam Map is computed from the cumulative sum of energy values of an image. The proposed method uses seam importance map along with the weighted average of various color channels of Lab color space namely boundary aware color map to extract the saliency map. These two maps are combined and further optimized to get the final saliency output using the optimization technique proposed in a previous study. Some intermediate combinations which are closer to the proposed optimized version but differ in the optimization technique are also presented in this paper. Several standard benchmark datasets including the famous MSRA 10k dataset are used to evaluate performance of the suggested procedure. Precision-recall curve and F-beta values found from the experiments on those datasets and comparison with other state of the art techniques prove the superiority of the proposed method. |
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},
url = {https://ieeexplore.ieee.org/abstract/document/9036637},
doi = {10.1109/IC4ME247184.2019.9036637},
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. |
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://ieeexplore.ieee.org/document/8660825},
doi = {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. |
