Abstract
The co-occurrence features learned through pattern mining methods have more discriminative power to separate images from other categories than individual low-level features. However, the “pattern explosion” problem involved in mining process prevents its application in many visual tasks. In this paper, we propose a novel scheme to learn discriminative features based on a mined optimal pattern model. The proposed method deals with the “pattern explosion” problem from two aspects, (1) it uses selected weak semantic patches instead of grid patches to substantially reduce the database to mine; (2) the adopted optimal pattern model can produce compact and representative patterns which make the resulted image code more effective and discriminative for classification. In our work, we apply the minimal description length (MDL) to mine the optimal pattern model. We evaluate the proposed method on two publicly available datasets (15-Scenes and Oxford-Flowers17) and the experimental results demonstrate its effectiveness.
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References
Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: 2003 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1470–1477. IEEE (2003)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893. IEEE (2005)
Savarese, S., Winn, J., Criminisi, A.: Discriminative object class models of appearance and shape by correlatons. In: 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2033–2040. IEEE (2006)
Yao, B., Fei-Fei, L.: Grouplet: A structured image representation for recognizing human and object interactions. In: 2003 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9–16. IEEE (2010)
Liu, D., Hua, G., Viola, P., Chen, T.: Integrated feature selection and higher-order spatial feature extraction for object categorization. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)
Yang, Y., Newsam, S.: Spatial pyramid co-occurrence for image classification. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1465–1472. IEEE (2011)
Fernando, B., Fromont, E., Tuytelaars, T.: Mining mid-level features for image classification. Int. J. Comput. Vision 108, 186–203 (2014)
Voravuthikunchai, W., Crémilleux, B., Jurie, F.: Histograms of pattern sets for image classification and object recognition. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 224–231. IEEE (2014)
Jiang, Y., Meng, J., Yuan, J.: Randomized visual phrases for object search. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3100–3107. IEEE (2012)
Zhang, S., Huang, Q., Hua, G., Jiang, S., Gao, W., Tian, Q.: Building contextual visual vocabulary for large-scale image applications. In: 2010 ACM International Conference on Multimedia, pp. 501–510. ACM (2010)
Mita, T., Kaneko, T., Stenger, B., Hori, O.: Discriminative feature co-occurrence selection for object detection. In: 2008 IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1257–1269. (2008)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multi-view object detection. In: 2007 IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 854–869. (2007)
Yuan, J., Yang, M., Wu, Y.: Mining discriminative co-occurrence patterns for visual recognition. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2777–2784. IEEE (2011)
Weng, C., Yuan, J.: Efficient mining of optimal AND/OR patterns for visual recognition. IEEE Trans. Multimedia 17(5), 626–635 (2015)
Zuo, Z., Wang, G., Shuai, B., Zhao, L., Yang, Q., Jiang, X.: Learning discriminative and shareable features for scene classification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 552–568. Springer, Heidelberg (2014)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), pp. 722–729. IEEE (2008)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2169–2178. IEEE (2006)
Grünwald, P.: A tutorial introduction to the minimum description length principle. In: Advances in Minimum Description Length: Theory and Applications, pp. 23–81 (2005)
Vreeken, J., Van Leeuwen, M., Siebes, A.: Krimp: mining itemsets that compress. Data Min. Knowl. Disc. 23(1), 169–214 (2011)
Ko, Y.: A study of term weighting schemes using class information for text classification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1029–1030. ACM (2012)
Singh, S., Gupta, A., Efros, A.A.: Unsupervised discovery of mid-level discriminative patches. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 73–86. Springer, Heidelberg (2012)
Sun, J., Ponce, J.: Learning discriminative part detectors for image classification and cosegmentation. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3400–3407. IEEE (2013)
Van de Sande, K.E., Uijlings, J.R., Gevers, T., Smeulders, A.W.: Segmentation as selective search for object recognition. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1879–1886. IEEE (2011)
Wang, M., Liu, X., Wu, X.: Visual Classification by l 1 -Hypergraph Modeling. In: 2015 IEEE Transactions on Knowledge and Data Engineering, pp. 2564–2574. IEEE (2015)
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823–870 (2007)
Wang, J., Wang, M., Li, P., Liu, L., Zhao, Z., Hu, X., Wu, X.: Online feature selection with group structure analysis. IEEE Trans. Knowl. Data Eng.
Acknowledgements
This work was partially supported by National Natural Science Funds of China (61173104, 61472059, 61428202).
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Liu, L., Bao, Y., Li, H., Fan, X., Luo, Z. (2016). Discriminative Feature Learning with an Optimal Pattern Model for Image Classification. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_57
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