文件列表:

000001.jpg (78771, 2016-08-19)
BINGMultiple.cpp (2331, 2016-08-19)
BINGMultiple.mexa64 (348418, 2016-08-19)
BINGSingle.cpp (2129, 2016-08-19)
BINGSingle.mexa64 (348254, 2016-08-19)
Example_BINGMultiple.m (155, 2016-08-19)
Example_BINGSingle.m (974, 2016-08-19)
Example_trainBING.m (126, 2016-08-19)
MxArray.cpp (19293, 2016-08-19)
MxArray.hpp (36883, 2016-08-19)
Objectness (0, 2016-08-19)
Objectness\LICENSE (1505, 2016-08-19)
Objectness\Src (0, 2016-08-19)
Objectness\Src\CMakeLists.txt (1344, 2016-08-19)
Objectness\Src\CMakeLists.txt.user (11646, 2016-08-19)
Objectness\Src\CmFile.cpp (4806, 2016-08-19)
Objectness\Src\CmFile.h (2718, 2016-08-19)
Objectness\Src\CmShow.cpp (2422, 2016-08-19)
Objectness\Src\CmShow.h (258, 2016-08-19)
Objectness\Src\CmTimer.h (2319, 2016-08-19)
Objectness\Src\DataSetVOC.cpp (6674, 2016-08-19)
Objectness\Src\DataSetVOC.h (2370, 2016-08-19)
Objectness\Src\FilterTIG.cpp (2794, 2016-08-19)
Objectness\Src\FilterTIG.h (1752, 2016-08-19)
Objectness\Src\ImgContrastBB.h (1723, 2016-08-19)
Objectness\Src\LibLinear (0, 2016-08-19)
Objectness\Src\LibLinear\LibLinear.vcxproj (4226, 2016-08-19)
Objectness\Src\LibLinear\blas (0, 2016-08-19)
Objectness\Src\LibLinear\blas\Makefile (293, 2016-08-19)
Objectness\Src\LibLinear\blas\blas.h (702, 2016-08-19)
Objectness\Src\LibLinear\blas\blasp.h (16460, 2016-08-19)
Objectness\Src\LibLinear\blas\daxpy.c (1205, 2016-08-19)
Objectness\Src\LibLinear\blas\ddot.c (1211, 2016-08-19)
Objectness\Src\LibLinear\blas\dnrm2.c (1306, 2016-08-19)
Objectness\Src\LibLinear\blas\dscal.c (1035, 2016-08-19)
Objectness\Src\LibLinear\linear.cpp (55369, 2016-08-19)
Objectness\Src\LibLinear\linear.h (2008, 2016-08-19)
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# BING Objectness BING Objectness proposal estimator Matlab (mex-c) wrapper, runs at 250 FPS at a i7 CPU (2.93Hz) with Ubuntu 12.04 ***-bit and Matlab R2013a. ## Introduction This is the matlab wrapper of BING Objectness for efficient objectness proposal estimator following the CVPR 2014 paper BING, please consider to cite and refer to this paper. @inproceedings{BingObj2014, title={{BING}: Binarized Normed Gradients for Objectness Estimation at 300fps}, author={Ming-Ming Cheng and Ziming Zhang and Wen-Yan Lin and Philip H. S. Torr}, booktitle={IEEE CVPR}, year={2014}, } The original author Ming-Ming Cheng has already released the source code for windows ***-bit platform, and Shuai Zheng has provided the code for the linux/mac/windows users. In this library, I intend to provide some simple functions so that users in Matlab can easily reproduce the results in the paper or use BING for some other applications. ## Requirements In order to make the code running, you need to download the images/annotations PASCAL VOC 2007 data from the link: [VOC2007](http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/#testdata). Please refer to the FAQs #2 in [http://mmcheng.net/bing/](http://mmcheng.net/bing/) for more details about how to prepare for the VOC2007 dataset. ## HowTo in Matlab I have written three mex-c fuctions: * trainBING.cpp - For training BING Objectness * BINGMultiple.cpp - For reproducing the results of the original paper * BINGSingle.cpp - For users to use BING on a single image Also, I have written three matlab scripts to show how to use them in Matlab (Name Convention: Example\_ + function name ). Note that the function __trainBING__ is a bit needless because __BINGMultiple__ and __BINGSingle__ themselves will learn the models if they do not exists. You can run Example\_BINGMultiple.m to reproduce the results in the origin paper, and a script called __PerImgAll.m__ will be generated in your VOC2007 folder. You can use the script as well as __PlotsCVPR14.m__ to plot the Figure 3 in the paper. I have tested the code in Ubuntu 12.04 ***-bit (8G Memory) and Matlab R2013a, and it produces the same accuarcy results as the original windows version, except that in my PC, it runs at 250 FPS compared to 300 FPS reported in the paper. If you intend to use the codes in other system version, please run _compile.m_ to re-compile the files. Please contact me (removethisifyouarehuman-tfzhou@bit.edu.cn) or create an issue if you have problems to run the codes. ## Other Source Code Repos * [http://mmcheng.net/bing/](http://mmcheng.net/bing/) -- The original code / FAQ / Paper by Ming-Ming Cheng. * [https://github.com/bittnt/Objectness](https://github.com/bittnt/Objectness) -- The Linux version library by Shuai Zheng. ## License BSD license.

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