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Erschienen in:
The Curious Robot: Learning Visual Representations via Physical Interactions Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Image Co-localization by Mimicking a Good Detector’s Confidence Score Distribution

verfasst von : Yao Li, Lingqiao Liu, Chunhua Shen, Anton van den Hengel

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-arts.

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Vorheriges Kapitel The Curious Robot: Learning Visual Representations via Physical Interactions
Nächstes Kapitel Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understanding
Fußnoten
1
In our case two superpixels are connected if the distance between their centroids is smaller than the sum of their major axis length.
 
2
The six categories are chipmunk, rhino, stoat, racoon, rake and wheelchair. Note that ground-truth bounding box annotations are available for these categories, thus enable quantitative evaluation.
 
Literatur
1.
Bilen, H., Pedersoli, M., Tuytelaars, T.: Weakly supervised object detection with convex clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1081–1089 (2015)
2.
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)CrossRef
3.
Chen, X., Shrivastava, A., Gupta, A.: Enriching visual knowledge bases via object discovery and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2035–2042 (2014)
4.
Cho, M., Kwak, S., Schmid, C., Ponce, J.: Unsupervised object discovery and localization in the wild: part-based matching with bottom-up region proposals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1201–1210 (2015)
5.
Cinbis, R.G., Verbeek, J.J., Schmid, C.: Multi-fold MIL training for weakly supervised object localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2409–2416 (2014)
6.
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
7.
Deselaers, T., Alexe, B., Ferrari, V.: Weakly supervised localization and learning with generic knowledge. Int. J. Comput. Vis. 100(3), 275–293 (2012)MathSciNetCrossRef
8.
Everingham, M., Eslami, S.M.A., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The PASCAL visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2015)CrossRef
9.
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRef
10.
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
11.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016)CrossRef
12.
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)CrossRef
13.
Hoiem, D., Chodpathumwan, Y., Dai, Q.: Diagnosing error in object detectors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 340–353. Springer, Heidelberg (2012)
14.
Hosang, J.H., Benenson, R., Dollár, P., Schiele, B.: What makes for effective detection proposals? IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 814–830 (2016)CrossRef
15.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:​1408.​5093 (2014)
16.
Joulin, A., Bach, F.R., Ponce, J.: Discriminative clustering for image co-segmentation. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 1943–1950 (2010)
17.
Joulin, A., Tang, K., Fei-Fei, L.: Efficient image and video co-localization with Frank-Wolfe algorithm. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 253–268. Springer, Heidelberg (2014)
18.
Krause, J., Jin, H., Yang, J., Li, F.: Fine-grained recognition without part annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5546–5555 (2015)
19.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1106–1114 (2012)
20.
Küttel, D., Ferrari, V.: Figure-ground segmentation by transferring window masks. In: Proceedings of the EEE Conference on Computer Vision and Pattern Recognition, pp. 558–565 (2012)
21.
Kwak, S., Cho, M., Ponce, J., Schmid, C., Laptev, I.: Unsupervised object discovery and tracking in video collections. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3173–3181 (2015)
22.
Parkhi, O.M., Vedaldi, A., Jawahar, C.V., Zisserman, A.: The truth about cats and dogs. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1427–1434 (2011)
23.
Prest, A., Leistner, C., Civera, J., Schmid, C., Ferrari, V.: Learning object class detectors from weakly annotated video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3282–3289 (2012)
24.
Ren, W., Huang, K., Tao, D., Tan, T.: Weakly supervised large scale object localization with multiple instance learning and bag splitting. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 405–416 (2016)CrossRef
25.
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)CrossRef
26.
Rubinstein, M., Joulin, A., Kopf, J., Liu, C.: Unsupervised joint object discovery and segmentation in internet images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1939–1946 (2013)
27.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef
28.
Shi, Z., Hospedales, T.M., Xiang, T.: Bayesian joint topic modelling for weakly supervised object localisation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2984–2991 (2013)
29.
Siva, P., Xiang, T.: Weakly supervised object detector learning with model drift detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 343–350 (2011)
30.
Tang, K., Joulin, A., Li, L., Li, F.: Co-localization in real-world images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1464–1471 (2014)
31.
Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)CrossRef
32.
Galleguillos, C., Babenko, B., Rabinovich, A., Belongie, S., Wang, C., Ren, W., Huang, K., Tan, T.: Weakly supervised object localization with latent category learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 431–445. Springer, Heidelberg (2014)
33.
Wang, X., Zhu, Z., Yao, C., Bai, X.: Relaxed multiple-instance SVM with application to object discovery. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1224–1232 (2015)
34.
Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014)
Metadaten
Titel
Image Co-localization by Mimicking a Good Detector’s Confidence Score Distribution
verfasst von
Yao Li
Lingqiao Liu
Chunhua Shen
Anton van den Hengel
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46474-9

Electronic ISBN: 978-3-319-46475-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46475-6_2