Global contrast based salient region detection Ming Ming Cheng,  Niloy J. Mitra,  Xiaolei Huang,  Philip H. S. Torr,  Shi Min Hu. Abstract. Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object extraction algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi scale, and produces full resolution, high quality saliency maps. These saliency maps are further used to initialize a novel iterative version of Grab. Cut for high quality salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms existing salient object detection and segmentation methods, yielding higher precision and better recall rates. MIDOP/manual/images/uploading_data_into_mysql_clip_image020.png' alt='Matlab 2007 For Windows 7 32 Bit' title='Matlab 2007 For Windows 7 32 Bit' />We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch based image retrieval SBIR via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state of the art SBIR methods, and additionally provides important target object region information. Matlab 2007 For Windows 7 32 Bit' title='Matlab 2007 For Windows 7 32 Bit' />Papers. Most related projects on this website Efficient Salient Region Detection with Soft Image Abstraction. Ming Ming Cheng, Jonathan Warrell, Wen Yan Lin, Shuai Zheng, Vibhav Vineet, Nigel Crook. IEEE International Conference on Computer Vision IEEE ICCV, 2. FM5r_pAU/U8To7FVxcDI/AAAAAAAAA3w/jVsFvf4gYLw/s1600/Matlab2014.png' alt='Matlab 2007 For Windows 7 32 Bit' title='Matlab 2007 For Windows 7 32 Bit' />Back to Commercial Stabilized HeNe Lasers SubTable of Contents. Forward to HeNe Laser Testing, Adjustment, Repair. Introduction This chapter deals with stabilized. Figure. Statistical comparison results of a different saliency region detection methods, b their variants, and c object of interest region segmentation methods. The latest oscilloscope and data logger news from Pico. OxMetrics tm is a family of of software packages providing an integrated solution for the econometric analysis of time series, forecasting, financial econometric. Source Sdk Base 2006 there. Matlab 2007 For Windows 7 32 Bit' title='Matlab 2007 For Windows 7 32 Bit' />Project page bib latex official versionBING Binarized Normed Gradients for Objectness Estimation at 3. Ming Ming Cheng, Ziming Zhang, Wen Yan Lin, Philip H. S. Torr, IEEE International Conference on Computer Vision and Pattern Recognition IEEE CVPR, 2. Project pagepdfbib Oral, Accept rate 5. Salient. Shape Group Saliency in Image Collections. Matlab 2007 For Windows 7 32 Bit' title='Matlab 2007 For Windows 7 32 Bit' />Ming Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Shi Min Hu. The Visual Computer 3. Project page bib latex Official versionDownloads. Some files are zip format with password. Read the notes to see how to get the password. Data. The MSRA1. 0K benchmark dataset a. THUS1. 00. 00 comprises of per pixel ground truth annotation for 1. MSRA images 1. 81 MB, each of which has an unambiguous salient object and the object region is accurately annotated with pixel wise ground truth labeling 1. M. We provide saliency maps 5. GB containing 1. 70, 0. FT 1, AIM 2, MSS 3, SEG 4, Se. R 5, SUN 6, SWD 7, IM 8, IT 9, GB 1. SR 1. 1, CA 1. LC 1. AC 1. CB 1. 5. Saliency segmentation 7. MB results for FT1, SEG4, and CB1. Shark Week 2015 Monster Mako. Windows executable. We supply an windows msi for install our prototype software, which includes our implementation for FT2, SR1. LC2. 8, our HC, RC and saliency cut method. C source code. The C implementation of our paper as well as several other state of the art works. Supplemental material. Supplemental materials 6. MB including comparisons with other 1. Salient object detection results for images with multiple objects. We tested it on the dataset provided by the CVPR 2. Image Segmentation by Probabilistic Bottom Up Aggregation and Cue Integration. More results for recent methods. If anyone want to share their results on our MSRA1. K benchmark facilitate other researchers to compare with recent methods, please contact me via email see the header image of this project page for it. I will put your results as well as paper links in this page. Comparisons with state of the art methods. Figure. Statistical comparison results of a different saliency region detection methods, b their variants, and c object of interest region segmentation methods, using largest public available dataset i and ii our MSRA1. K dataset to be made public available. We compare our HC method and RC method with 1. FT 1, AIM 2, MSS 3, SEG 4, Se. R 5, SUN 6, SWD 7, IM 8, IT 9, GB 1. SR 1. 1, CA 1. LC 1. AC 1. CB 1. 5. We also take simple variable size Gaussian model Gau and Grab. Cut method as a baseline. Please see our paper for detailed explaintionsFigure. Comparison of average F for different saliency segmentation methods FT 1, SEG 4, and ours, on THUR1. K dataset, which is composed by non selected internet images. Method. ITAIMIMMSSSEGSe. RSUNSWDCBTime s0. Code. Matlab. Matlab. Matlab. Matlab. Matlab. Matlab. Matlab. Matlab. M CMethod. GBSRFTACCALCHCRCTime s1. Code. Matlab. Matlab. CMatlab. Matlab. CCCTable. Average time taken to compute a saliency map for images in the MSRA1. K database. Note that we use the authors original implementations for MSS and FT, which is not well optimized code. Method. TimesCode Type. FT0. 2. 47. Matlab. SEG7. 4. 8M CCB3. M COur. 0. 6. CTable. Comparison of average time for different saliency segmentation methods. Figure. Saliency maps computed by different state of the art methodsb p, and with our proposed HCq and RC methodsr. Most results highlight edges, or are of low resolution. See also the shared data for saliency detection results for the whole MSRA1. K dataset. Figure. Sketch based image comparison. In each group from left to right, first column shows images download from Flickr using the corresponding keyword second column shows our retrieval results obtained by comparing user input sketch with Saliency. Cut result using shape context measure 4. SHo. G 4. 2. FAQs. Until now, more than 2. Some of them have questions about using the code. Here are some frequently asked questions some of them are frequently asked questions from many reviewers as well for new users to refer Q1 Im confused with the sentence in the paper In our experiments, the threshold is chosen empirically to be the threshold that gives 9. But all most the case, people have not the ground truth, so cannot compute the call rate. When I use your Cut application, I need to guess threshold value to have good cut image. A The recall rate is just used to evaluate the algorithm. When you use it, you typically dont have to evaluate the algorithm itself very often. This sentence is used to explain what the fixed threshold we use typically means. Actually, when initialized using RC saliency maps, this threshold is 7. It doesnt mean that the saliency values corresponds to recall rate of 9. So, just use the suggested threshold of 7. OK. Q2 I use your code to get results for the same database you used. But the results seem to have some small difference from yours. A It seems that the cvt. Color function in Open. CV 1. x is different from those in Open. Cv 2. X. I suggest users to use those in recent versions. The segmentation method I used sometimes generates strange results, leading to strange results of saliency maps. This happens at low frequency. When this happens, I rerun the exe again and it becomes OK. I dont know why, but this really happens when I use the exe first time after compiling Very strange, maybe because some default initializations. If someone find the bug, please report to me. Q3 Does your algorithm only get good results for images with single salient object A Mostly yes.