@article{filali_multi-graph_2016,

title = {Multi-graph based salient object detection},

author = {I. Filali and M. S. Allili and N. Benblidia},

editor = {Karray F. Campilho A. Campilho A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978804496&doi=10.1007%2f978-3-319-41501-7_36&partnerID=40&md5=eb519756d2e72245e4131d5dc0b416b5},

doi = {10.1007/978-3-319-41501-7_36},

issn = {03029743},

year  = {2016},

date = {2016-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9730},

pages = {318–324},

abstract = {We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.},

note = {ISBN: 9783319415000 

Publisher: Springer Verlag},

keywords = {Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects},

pubstate = {published},

tppubtype = {article}

}

@article{filali_multi-scale_2016,

title = {Multi-scale salient object detection using graph ranking and global–local saliency refinement},

author = {I. Filali and M. S. Allili and N. Benblidia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982091007&doi=10.1016%2fj.image.2016.07.007&partnerID=40&md5=60dabe68b5cff4b5d00216d6a632e1cd},

doi = {10.1016/j.image.2016.07.007},

issn = {09235965},

year  = {2016},

date = {2016-01-01},

journal = {Signal Processing: Image Communication},

volume = {47},

pages = {380–401},

abstract = {We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.},

note = {Publisher: Elsevier B.V.},

keywords = {Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection},

pubstate = {published},

tppubtype = {article}

}