Salient Object Detection Techniques in Computer Vision—A Survey
Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the fi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/10/1174 |
| _version_ | 1797550562439331840 |
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| author | Ashish Kumar Gupta Ayan Seal Mukesh Prasad Pritee Khanna |
| author_facet | Ashish Kumar Gupta Ayan Seal Mukesh Prasad Pritee Khanna |
| author_sort | Ashish Kumar Gupta |
| collection | DOAJ |
| description | Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end. |
| first_indexed | 2024-03-10T15:31:07Z |
| format | Article |
| id | doaj.art-fcfd3872624d4f509b8767126972e977 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T15:31:07Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-fcfd3872624d4f509b8767126972e9772023-11-20T17:42:29ZengMDPI AGEntropy1099-43002020-10-012210117410.3390/e22101174Salient Object Detection Techniques in Computer Vision—A SurveyAshish Kumar Gupta0Ayan Seal1Mukesh Prasad2Pritee Khanna3PDPM-Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Dumna Airport Road, Jabalpur 482005, IndiaPDPM-Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Dumna Airport Road, Jabalpur 482005, IndiaCentre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Broadway, Sydney, NSW 2007, AustraliaPDPM-Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Dumna Airport Road, Jabalpur 482005, IndiaDetection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.https://www.mdpi.com/1099-4300/22/10/1174salient object detectionsaliency cues, conventional salient object detection modelsdeep learning-based salient object detection models |
| spellingShingle | Ashish Kumar Gupta Ayan Seal Mukesh Prasad Pritee Khanna Salient Object Detection Techniques in Computer Vision—A Survey Entropy salient object detection saliency cues, conventional salient object detection models deep learning-based salient object detection models |
| title | Salient Object Detection Techniques in Computer Vision—A Survey |
| title_full | Salient Object Detection Techniques in Computer Vision—A Survey |
| title_fullStr | Salient Object Detection Techniques in Computer Vision—A Survey |
| title_full_unstemmed | Salient Object Detection Techniques in Computer Vision—A Survey |
| title_short | Salient Object Detection Techniques in Computer Vision—A Survey |
| title_sort | salient object detection techniques in computer vision a survey |
| topic | salient object detection saliency cues, conventional salient object detection models deep learning-based salient object detection models |
| url | https://www.mdpi.com/1099-4300/22/10/1174 |
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