Abstract
This paper presents an efficient method for object detection in natural scenes. It is accomplished via generalized Hough transform of distinctive midlevel parts. These parts are more meaningful than low-level patches such as lines or corners and would be able to cover the key structures of object. We collect the initial sets of parts by clustering with k-means in WHO space and train LDA model for every cluster. The codebooks are generated by applying the trained detectors to discover parts in whole positive training images and storing their spatial distribution relative to object center. When detecting in a new image, the energy map is formed by the voting from every entry in codebook and is used to predict the location of object. Experiment result shows the effectiveness of the proposed scheme.
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References
Malisiewicz, T., Gupta, A., Efros, A.A.: Ensemble of exemplar-SVMs for object detection and beyond. In: IEEE International Conference on Computer Vision, pp. 89–96 (2011)
Maji, S., Shakhnarovich, G.: Part discovery from partial correspondence. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 931–938 (2013)
Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. International Journal of Computer Vision 77(1-3), 259–289 (2008)
Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1022–1029 (2009)
Gall, J., Yao, A., Razavi, N., Van Gool, L.: Hough forests for object detection, tracking, and action recognitions. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(11), 2188–2202 (2011)
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)
Hariharan, B., Malik, J., Ramanan, D.: Discriminative Decorrelation for clustering and classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 459–472. Springer, Heidelberg (2012)
Juneja, M., Vedaldi, A., Jawahar, C.V., Zisserman, A.: Blocks that shout: Distinctive parts for scene classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 923–930 (2013)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1627–1645 (2010)
Yao, C., Bai, X., Liu, W., Latecki, L.J.: Human Detection using Learned Part Alphabet and Pose Dictionary. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 251–266. Springer, Heidelberg (2014)
Wang, X.G., Wang, B.Y., Bai, X., Liu, W.Y., Tu, Z.W.: Max-Margin Multiple Instance Dictionary Learning. In: Proceedings of the 30th International Conference on Machine Learning, pp. 846–854 (2013)
Dollár, P., Babenko, B., Belongie, S., Perona, P., Tu, Z.: Multiple component learning for object detection. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 211–224. Springer, Heidelberg (2008)
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Kuang, X., Sang, N., Chen, F., Wang, R., Gao, C. (2014). Hough Voting with Distinctive Mid-Level Parts for Object Detection. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_31
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DOI: https://doi.org/10.1007/978-3-662-45646-0_31
Publisher Name: Springer, Berlin, Heidelberg
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