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| 1 | Salient object detection: A survey显示文摘Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions. | Ali Borji Ming-Ming Cheng Qibin Hou Huaizu Jiang Jia Li | 2019 | Computational Visual Media2019，5，2： | 40 |
| 2 | Saliency Rank:Two-stage manifold ranking for salient object detection显示文摘Salient object detection remains one of the most important and active research topics in computer vision,with wide-ranging applications to object recognition,scene understanding,image retrieval,context aware image editing,image compression,etc. Most existing methods directly determine salient objects by exploring various salient object features.Here,we propose a novel graph based ranking method to detect and segment the most salient object in a scene according to its relationship to image border(background) regions,i.e.,the background feature.Firstly,we use regions/super-pixels as graph nodes,which are fully connected to enable both long range and short range relations to be modeled. The relationship of each region to the image border(background) is evaluated in two stages:(i) ranking with hard background queries,and(ii) ranking with soft foreground queries. We experimentally show how this two-stage ranking based salient object detection method is complementary to traditional methods,and that integrated results outperform both. Our method allows the exploitation of intrinsic image structure to achieve high quality salient object determination using a quadratic optimization framework,with a closed form solution which can be easily computed.Extensive method evaluation and comparison using three challenging saliency datasets demonstrate that our method consistently outperforms 10 state-of-theart models by a big margin. | Wei Qi Ming-Ming Cheng Ali Borji Huchuan Lu Lian-Fa Bai | 2015 | Computational Visual Media2015，1，4： | 5 |
| 3 | Joint salient object detection and existence prediction显示文摘Recent advances in supervised salient object detection modeling has resulted in significant performance improvements on benchmark datasets. However, most of the existing salient object detection models assume that at least one salient object exists in the input image. Such an assumption often leads to less appealing saliency maps on the background images with no salient object at all. Therefore, handling those cases can reduce the false positive rate of a model. In this paper, we propose a supervised learning approach for jointly addressing the salient object detection and existence prediction problems. Given a set of background-only images and images with salient objects, as well as their salient object annotations, we adopt the structural SVM framework and formulate the two problems jointly in a single integrated objective function: saliency labels of superpixels are involved in a classification term conditioned on the salient object existence variable, which in turn depends on both global image and regional saliency features and saliency labels assignments. The loss function also considers both image-level and regionlevel mis-classifications. Extensive evaluation on benchmark datasets validate the effectiveness of our proposed joint approach compared to the baseline and state-of-the-art models. | Huaizu JIANG Ming-Ming CHENG Shi-Jie LI Ali BORJI Jingdong WANG | 2019 | Frontiers of Computer Science2019，13，4： | 2 |
| 4 | Online learning of task-driven object-based visual attention control 显示文摘 | Borji Ali Ahmadabadi Majid Nili Araabi Babak Nadjar | 2010 | Image and Vision Computing(S0262-8856)2010，28，7： | 1 |
| 5 | State - of - the - art in visual attention modeling 显示文摘 | BORJI A I333 L | 2013 | IEEE Transactions on Pattern Analysis and Machine Intelli- gence2013，35，1： | 1 |
| 6 | Safranal Treatment Improves Hyperglycemia, Hyperlipidemia and Oxidative Stress in Streptozotocin-Induced Diabetic Rats显示文摘 | Samarghandian S Borji A Delkhosh MB | 2013 | J Pharm Pharm Sci2013，16，2： | 1 |
| 7 | State-of-the-art in Visual Attention Modeling 显示文摘 | BORJI A ITTI L | 2013 | Pattern Analysis and Machine Intelli- gence IEEE Transactions on2013，35，1： | 1 |
| 8 | Novel two-layer millimeter-waveslot array antennas based on substrate integrated waveguides 显示文摘 | Bakhtafrooz A Borji A Busuioc D | 2010 | Pro-gress in Electromagnetics Research2010，10，： | 1 |
| 9 | Modelling and Pareto optimization of heat transfer and flow eoeffeients in microchannels using GMDH type neural networks and genetic algorithms 显示文摘 | AMANIFARD N NARIMAN-ZADEH N BORJI M | 2008 | Energy Conversion and Management2008，49，2： | 1 |
| 10 | Effect of royal jelly in vivo injection on embryonic growth,hatchability,and gonadotropin levels of pullet breeder chicks显示文摘 | Moghaddam A Karimi I Borji M | | 0，，： | 1 |
| 11 | Safranal treatment im- proves hyperglycemia,hyperlipidemia and oxidative stress in streptozoto- cin-induced diabetic rats显示文摘 | Samarghandian S Borji A Delkhosh MB | 2013 | J Pharm Sci2013，16，2： | 1 |
| 12 | Source Apportionment of Volatile Organic Compounds in Tehran, Iran显示文摘 | Maryam Sarkhosh Amir Hossein Mahvi Masud Yunesian Ramin Nabizadeh Saeedeh Hemmati Borji Ali Ghiami Bajgirani | 2013 | Bulletin of Environmental Contamination and Toxicology2013，，4： | 1 |
| 13 | Innovative restriction site created PCR-RFLP for detection of benzimidazole resistance in Teladorsagia circumclncta 显示文摘 | SHAYAN P ESLAMI A BORJI H | 2007 | Parasitol Res2007，100，5： | 1 |
| 14 | Exploiting local and global patch rari-ties for saliency detection显示文摘 | Ali Borji Laurent Itti | 2012 | IEEE Conference on ComputerVision and Pattern Recognition2012，，： | 1 |
| 15 | Epidemiical survey of vulvovaginal candidosis in Sfax,Tunisia显示文摘 | Amouri I Sellami H Borji N | 2011 | Mycoses2011，54，5： | 1 |
| 16 | State-oPthe-art in visual attention modeling显示文摘 | Borji A Itti L | | IEEE0，，： | 1 |
| 17 | S'afranal treat- merit improves hyperglycemia, hyperlipidemia and oxidative stress in streptozotocin-induceddiabetic rats 显示文摘 | Samarghandian S Borji A Delkhosh MB | 2013 | J Pharm Pharm Sci2013，16，2： | 1 |
| 18 | State-of-the-art in visual attention modeling 显示文摘 | Borji A Itti L | 2013 | Pattern Analysis and Machine Intelligence IEEE Transactions on2013，35，1： | 1 |
| 19 | End effect in the explosive forming of tubes显示文摘 | Moshksar M M Borji S | 1994 | Journal of Materials Processing Technology1994，42，： | 1 |
| 20 | Quantitative Analysis ofHuman-Model Agreement in Visual SaliencyModeling : A Comparative Study 显示文摘 | Borji A Sihite D N Itti L | 2012 | IEEE Transac-tions on Image Processing2012，116，： | 1 |
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