TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Salient Object Detection via Adaptive Region Merging |
| 영문제목(English Title) |
Salient Object Detection via Adaptive Region Merging |
| 저자(Author) |
Jingbo Zhou
Jiyou Zhai
Yongfeng Ren
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| 원문수록처(Citation) |
VOL 10 NO. 09 PP. 4386 ~ 4404 (2016. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Most existing salient object detection algorithms commonly employed segmentation
techniques to eliminate background noise and reduce computation by treating each segment as a processing unit. However, individual small segments provide little information about global contents. Such schemes have limited capability on modeling global perceptual phenomena. In this paper, a novel salient object detection algorithm is proposed based on region merging. An adaptive-based merging scheme is developed to reassemble regions based on their color dissimilarities. The merging strategy can be described as that a region R is merged with its adjacent region Q if Q has the lowest dissimilarity with Q among all Q's adjacent regions. To guide the merging process, superpixels that located at the boundary of the image are treated as the seeds. However, it is possible for a boundary in the input image to be occupied by the foreground object. To avoid this case, we optimize the boundary influences by locating and eliminating erroneous boundaries before the region merging. We show that even though three simple region saliency measurements are adopted for each region, encouraging performance can be obtained. Experiments on four benchmark datasets including MSRA-B, SOD, SED and iCoSeg show the proposed method results in uniform object enhancement and achieve state-of-the-art performance by comparing with nine existing methods.
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| 키워드(Keyword) |
Salient object detection
Image segmentation
Adaptive region merging
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| 파일첨부 |
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