An efficient segmentation method using saliency object detection
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| 001 | 60544787X | ||
| 003 | CHVBK | ||
| 005 | 20210128100134.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150801xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s11042-014-1871-z |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11042-014-1871-z | ||
| 245 | 0 | 3 | |a An efficient segmentation method using saliency object detection |h [Elektronische Daten] |c [Chongbo Zhou, Chuancai Liu] |
| 520 | 3 | |a Automatic co-segmentation is a challenging task because it lacks of prior cues. In this paper, an efficient region contrast based method is proposed for salient object detection and segmentation. The coarse location information of the salient object and the background is first estimated based on the distribution of the detected key-points. Histograms of the estimated foreground and background are calculated as their features. An image is then over-segmented into super-pixels and their histograms are computed. The saliency of a super-pixel is obtained according to the similarity coefficients between the super-pixel and the estimated foreground/background. With the saliency map, the salient object in the image is extracted using a graph cut based optimized framework. The proposed method is compared with state-of-the-art methods on the widely used dataset, and the experiments show that it overall obtains more accurate results. | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a Object segmentation |2 nationallicence | |
| 690 | 7 | |a Saliency detection |2 nationallicence | |
| 690 | 7 | |a Similarity measurement |2 nationallicence | |
| 700 | 1 | |a Zhou |D Chongbo |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, Jiangsu, China |4 aut | |
| 700 | 1 | |a Liu |D Chuancai |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, Jiangsu, China |4 aut | |
| 773 | 0 | |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/15(2015-08-01), 5623-5634 |x 1380-7501 |q 74:15<5623 |1 2015 |2 74 |o 11042 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11042-014-1871-z |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s11042-014-1871-z |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zhou |D Chongbo |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, Jiangsu, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Liu |D Chuancai |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, Jiangsu, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/15(2015-08-01), 5623-5634 |x 1380-7501 |q 74:15<5623 |1 2015 |2 74 |o 11042 | ||