@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}

}

@article{allili_globally_2007,

title = {Globally adaptive region information for automatic color-texture image segmentation},

author = {M. S. Allili and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548675168&doi=10.1016%2fj.patrec.2007.05.002&partnerID=40&md5=e1338223b9cb99afc35dfbfbf7859b72},

doi = {10.1016/j.patrec.2007.05.002},

issn = {01678655},

year  = {2007},

date = {2007-01-01},

journal = {Pattern Recognition Letters},

volume = {28},

number = {15},

pages = {1946–1956},

abstract = {In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.},

keywords = {Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures},

pubstate = {published},

tppubtype = {article}

}