Abstract
Region selection is able to boost the recognition performance for images with background clutter by discovering the object regions. In this paper, we propose a region selection method under the least squares framework. With the assumption that an object is a combination of several over-segmented regions, we impose a selection variable on each region, and employ a linear model to perform classification. The model parameter and the selection parameter are alternatively updated to minimize a sum-of-squares error function. During the iteration, the selection parameter can automatically pick the discriminant regions accounting for the object category, then fine tunes the linear model with the objects, independently of the background. As a result, the learnt model is able to distinguish object regions and non-object regions, which actually generates irregular-shape object localization. Our method performs significantly better than the baselines on two datasets, and the performance can be further improved when combining deep CNN features. Moreover, the algorithm is easy to implement and computationally efficient because of the merits inherited from the least squares.
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References
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 561–568 (2002)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)
Behmo, R., Marcombes, P., Dalalyan, A., Prinet, V.: Towards optimal naive bayes nearest neighbor. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 171–184. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_13
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference, BMVC 2014, Nottingham, UK, 1–5 September 2014 (2014)
Chen, Q., Song, Z., Dong, J., Huang, Z., Hua, Y., Yan, S.: Contextualizing object detection and classification. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 13–27 (2015)
Deselaers, T., Alexe, B., Ferrari, V.: Localizing objects while learning their appearance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 452–466. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_33
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 297–312. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_20
Hariharan, B., Arbeláez, P.A., Girshick, R.B., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June 2015, pp. 447–456 (2015)
Harzallah, H., Jurie, F., Schmid, C.: Combining efficient object localization and image classification. In: IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, 27 September–4 October 2009, pp. 237–244 (2009)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond sliding windows: object localization by efficient subwindow search. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Li, Y.-F., Kwok, J.T., Tsang, I.W., Zhou, Z.-H.: A convex method for locating regions of interest with multi-instance learning. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 15–30. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04174-7_2
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Ma, C., Huang, J., Yang, X., Yang, M.: Hierarchical convolutional features for visual tracking. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7–13 December 2015, pp. 3074–3082 (2015)
Nguyen, M.H., Torresani, L., de la Torre, L., Rother, C.: Weakly supervised discriminative localization and classification: a joint learning process. In: IEEE International Conference on Computer Vision, pp. 1925–1932 (2009)
Pandey, M., Lazebnik, S.: Scene recognition and weakly supervised object localization with deformable part-based models. In: IEEE International Conference on Computer Vision, pp. 1307–1314 (2011)
Russakovsky, O., Lin, Y., Yu, K., Fei-Fei, L.: Object-centric spatial pooling for image classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 1–15. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33709-3_1
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Tang, K.D., Sukthankar, R., Yagnik, J., Li, F.F.: Discriminative segment annotation in weakly labeled video. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2483–2490 (2013)
Vijayanarasimhan, S., Grauman, K.: Efficient region search for object detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1401–1408 (2011)
Viola, P.A., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: Advances in Neural Information Processing Systems, pp. 1–8 (2005)
Wang, L., Meng, D., Hu, X., Lu, J., Zhao, J.: Instance annotation via optimal bow for weakly supervised object localization. IEEE Trans. Cybern. 47, 1313–1324 (2017)
Wu, J., Yu, Y., Huang, C., Yu, K.: Deep multiple instance learning for image classification and auto-annotation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June 2015, pp. 3460–3469 (2015)
Xu, C., Xiong, C., Corso, J.J.: Streaming hierarchical video segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 626–639. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33783-3_45
Yakhnenko, O., Verbeek, J., Schmid, C.: Region-based image classification with a latent SVM model. INRIA Technical report, pp. 1–13 (2011)
Zhao, J., Wang, L., Cabral, R., la Torre, F.D.: Feature and region selection for visual learning. IEEE Trans. Image Process. 25, 1084–1094 (2016)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (NSFC) (No. 61703139), the Fundamental Research Funds for the Central Universities (No. 2016B12914), and the State Key Laboratory for Novel Software Technology (Nanjing University) (No. KFKT2017B09).
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Wang, L., Liu, Y., Lu, J. (2018). A Least Squares Approach to Region Selection. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_31
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