@article{audet_salient_2017,

title = {Salient object detection in images by combining objectness clues in the RGBD space},

author = {F. Audet and M. S. Allili and A. -M. Cretu},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022229105&doi=10.1007%2f978-3-319-59876-5_28&partnerID=40&md5=d78eb69cecd0a34ca2d517cfee44ef54},

doi = {10.1007/978-3-319-59876-5_28},

issn = {03029743},

year  = {2017},

date = {2017-01-01},

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

volume = {10317 LNCS},

pages = {247–255},

abstract = {We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.},

note = {ISBN: 9783319598758 

Publisher: Springer Verlag},

keywords = {Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines},

pubstate = {published},

tppubtype = {article}

}

@article{allili_image_2010,

title = {Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957964550&doi=10.1109%2fTCSVT.2010.2077483&partnerID=40&md5=d888c7fe52eff37a5744bccd6a4d3d9e},

doi = {10.1109/TCSVT.2010.2077483},

issn = {10518215},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Circuits and Systems for Video Technology},

volume = {20},

number = {10},

pages = {1373–1377},

abstract = {In this letter, we propose a clustering model that efficiently mitigates image and video under/over-segmentation by combining generalized Gaussian mixture modeling and feature selection. The model has flexibility to accurately represent heavy-tailed image/video histograms, while automatically discarding uninformative features, leading to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a database of real-world images and videos showed us the effectiveness of the proposed approach. © 2010 IEEE.},

keywords = {Clustering model, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Heavy-tailed, High dimensional spaces, Image and video segmentation, Image segmentation, image/video segmentation, Minimum message lengths, Real-world image, Video cameras},

pubstate = {published},

tppubtype = {article}

}