Abstract
Object detection methods based on sliding windows has long been considered a binary classification problem, but this formulation ignores order of examples. Deformable part models, which achieves great success in object detection, have the same problem.This paper aims to give better order to detections given by deformable part models. We use a bagged LambdaMART to model both pair-wise and list-wise relationships between detections. Experiments show our ranking models not only significantly improve detection rates compared to basic deformable part model detectors, but also outperform classification methods with same features.
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References
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 73–80. IEEE, June 2010
Azizpour, H., Laptev, I.: Object detection using strongly-supervised deformable part models. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 836–849. Springer, Heidelberg (2012)
Branson, S., Perona, P., Belongie, S.: Strong supervision from weak annotation: Interactive training of deformable part models. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1832–1839, November 2011
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Burges, C.: From ranknet to lambdarank to lambdamart: an overview. Learning 11, 23–581 (2010)
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96. ACM (2005)
Burges, C.J.C., Ragno, R., Le, Q.V.: Learning to rank with nonsmooth cost functions. In: NIPS, vol. 6, pp. 193–200 (2006)
Cheng, M.-M., Zhang, Z., Lin, W.-Y., Torr, P.H.S.: BING: Binarized normed gradients for objectness estimation at 300fps. In: IEEE CVPR (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005). De Europe
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge, (VOC2007) Results 92007). http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Ganjisaffar, Y., Caruana, R., Lopes, C.V.: Bagging gradient-boosted trees for high precision, low variance ranking models. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 85–94. ACM (2011)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2013). arXiv preprint arXiv:1311.2524
Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: IEEE 12th International Conference on Computer Vision, pp. 2146–2153. IEEE (2009)
Jia, Y.: Caffe: An open source convolutional architecture for fast feature embedding (2013). http://caffe.berkeleyvision.org/
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates Inc (2012)
van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders. Segmentation as selective search for object recognition. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1879–1886. IEEE (2011)
Wu, Q., Burges, C.J.C., Svore, K.M., Gao, J.: Ranking, boosting, and model adaptation. Tecnical report, MSR-TR-2008-109 (2008)
Zhu, L., Chen, Y., Yuille, A., Freeman, W.: Latent hierarchical structural learning for object detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1062–1069. IEEE (2010)
Acknowledgments
This work was partially supported by National Natural Science Foundation of China(Project No.61273365 and No. 61100120), the Fundamental Research Funds for the Central Universities (No. 2013RC0304), National High Technology Research and Development Program of China(No. 2012AA011104) and discipline building plan in 111 base(No. B08004) and Engineering Research Center of Information Networks, Ministry of Education.
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Sun, C., Wang, X., Lu, P. (2015). Object Ranking on Deformable Part Models with Bagged LambdaMART. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_5
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