An Efficient Salient Region Detection on Underwater Image with Grabcut Algorithm
Prasanna .S1, Durgadevi .S2, Senthilkumar .S3
1Prasanna.S, PG Scholar, ECE Department, E.G.S Pillay Engineering College, Nagappattinam, India.
2Durgadevi. S, Assistant professor, E.G.S Pillay Engineering college, Nagappattinam, India.
3Senthilkumar. S, Assistant professor, E.G.S Pillay Engineering college, Nagappattinam, India
Manuscript received on June 02, 2017. | Revised Manuscript received on June 05, 2017. | Manuscript published on June 15, 2017. | PP: 1-5 | Volume-4, Issue-11, June 2017. | Retrieval Number: K10360641117/2017©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Saliency detection aims at automatically estimating visually salient object regions in an image, saliency segmentation and foreground extraction are two important applications of this. However, it is a challenge for underwater images to estimate salient regions by saliency detection methods because of the low-contrast and poor quality. In this paper, we address this problem by combining the detected object regions rather than the whole image, where Sobel edge detector and Active contour is used for proposing candidate regions. We extensively evaluated our method on underwater images, and experimental results show that the performances of saliency detection and segmentation are improved. These saliency segmentation masks are further used to extract the foreground objects of an image.
Keywords: Saliency segmentation, foreground extraction, fish localization, underwater image.