Source: http://groups.inf.ed.ac.uk/calvin/objectness/
Timestamp: 2019-04-21 06:57:59+00:00

Document:
The objectness measure acts as a class-generic object detector. It quantifies how likely it is for an image window to contain an object of any class, such as cars and dogs, as opposed to backgrounds, such as grass and water. We release here software for computing objectness [1,2] and sampling any desired number of windows from an image according to their probability of containing an object.
For applications, we recommend to sample about 1000 windows, which ensures covering most objects even in very difficult images (e.g. with small objects and lots of clutter). However, in images of normal difficulty 100 windows are sufficient (e.g. images downloaded from image search engines).
From version V 1.5 we include a new window sampling strategy (NMS) which leads to higher detection rates. On the highly challenging PASCAL VOC 2007 dataset , the top 1000 sampled windows now cover 91% of all objects , as opposed to about 70% in previous versions using multinomial sampling .
In addition to the source code, we also release sampled windows for every image from PASCAL VOC 2007 , for both the new NMS sampling strategy and for the older multinomial sampling. These ready-to-use windows hopefully will facilitate applications on this dataset.
Objectness is computationally efficient. On a mid-range PC, it takes less than 3 seconds to compute the objectness measure and to sample 1000 windows, for an image of size 350 x 500.
For each of image we show the windows best covering the objects annotated in the PASCAL VOC 2007 (our of 1000 windows sampled with NMS). We mark in yellow windows correctly covering ground-truth objects (cyan); if there is more than one correct window, the best one is shown.
For each image we show its pixelwise objectness map. This is obtained by sampling 1000 windows using the NMS sampling procedure and accumulating them. We do this by computing for each image pixel the sum of the objectness scores for the sampled windows containing it. Objectness maps provide meaningful distributions over the object locations, demonstrating that it reduces their uncertainty.
 Alexe, B., Deselares, T. and Ferrari, V.
 Everingham, M., Van Gool, L., Williams, C., Winn, J., and Zisermann, A.
 Deselares, T., Alexe, B. and Ferrari, V.
 Khan, I., Roth, P. M. and Bischof, H.
 Alexe, B., Deselaers, T. and Ferrari, V.
 Vezhnevets, A., Ferrari, V. and Buhmann, J.
 Lee, Y. J. and Grauman, K.
 Prest, A., Schmid, C. and Ferrari, V.
 Sun, J., and Ling, H.
 Bao, X., Narayan, T., Sani, A. A., Richter, W., Choudhury, R. R., Zhong, L. and Satyanarayanan, M.
 Siva, P. and Xiang, T.
 Rahtu, E., Kannala, J. and Blaschko, M.
 Chang, K. Y., Liu, T. L., Chen, H. T., and Lai, S. H.
 Siva, P., Russell C., and Xing, T.
 Sener, F., Bas, C., and Ikizler-Cinbis, N.
1st Workshop on Action Recognition and Pose Estimation in Still Images, ECCV 2012.
 Stadler, S., Grabner, H., and Van Gool, L.
 Guillaumin, M., and Ferrari, V.
 Kuettel, D., and Ferrari, V.
 Kuettel, D., Guillaumin, M., and Ferrari, V.
 Spampinato, C., and Palazzo, S.
 Lu, Z., and Grauman, K.
 Blaschko, M., Kannala, J., and Rahtu, E.
 Siva, P., Russell, C., Xiang, T., and Agapito, L.
 Ristin, M., Gall, J., and Van Gool, L.
 Rubio, J., Serrat, J., Lopez, A., and Paragios, N.
 Meng, F., Li, H., Liu, G., and Ngan, K. N.
 Mai, L., Le, H., Niu, Y., and Liu, F.
 Saenko, K., Karayev, S., Jia, Y., Shyr, A., Janoch, A., Long, J., Fritz, M., and Dareell, T.
 Gao, Y., Zhang, J., Zhang, L., and Hu, Y.
 Shapovalova, N., Vahdat, A., Cannons, K., Lan, T., and Mori, G.
 Deselaers, T., Alexe, B., and Ferrari, V.
 Prest, A., Leistner, C., Civera, J., Schmid, C., and Ferrari, V.
 Loo, W. and Kim, T.K.
 Russakovsky, O., Deng, J., Huang, Z., Berg, A. and Fei-Fei, L.
Detecting avocados to zucchinis: what have we done, and where are we going?
 Jia, Y. and Han, M.
 Cinbis, R. G., Verbeek, J. and Schmid, C.
 Rubio, J. C., Serrat, J. and Lopez, A.
 Meng, F., Li, H., Ngan, K. N., Zeng, L. and Wu, Q.
 Karaoglu, S., Van Gemert, J. C. and Gevers, T.
 Jiang, P., Ling, H., Yu, J. and Peng, J.
 Li, L., Feng, W., Wan, L., and Zhang, J.

References: V.

 V.

 V.

 V. 
 V.

 V.

 V.

 V.

 V.

 V.