Abstract
In this paper, we propose an approach based on mixture of multiple components and mid-level part models for object detection in natural scenes. It is difficult to represent an object category with a monolithic model as the intra-variance in the category. To solve this, we use multi-component models and part models to describe the global variation and local deformation respectively. We obtain multi-components by clustering to form visual similar object group and training discriminant model for each one. The mid-level part models are learned automatically. We apply max-pooling to generate the feature vector using all part models and then train the SVM classifier based on these feature vectors. When detecting in image, we first achieve object candidates using multi-component models, and then the performance is refined by using part models and SVM classifier. Experiments on standard benchmarks demonstrate this coarse-to-fine detection system performs competitively.
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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., 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)
Maji, S., Malik, J.: Object detection using a max-margin hough transform. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1038–1045. Miami, FL (2009)
Kontschieder, P., Riemenschneider, H., Donoser, M., Bischof, H.: Discriminative learning of contour fragments for object detection. In: BMVC, pp. 1–12 (2011)
Razavi, N., Gall, J., Van Gool, L.: Scalable multi-class object detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1505–1512. IEEE (2011)
Razavi, N., Gall, J., Kohli, P., van Gool, L.: Latent hough transform for object detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 312–325. Springer, Heidelberg (2012)
Maji, S., Shakhnarovich, G.: Part discovery from partial correspondence. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 931–938. Portland, OR (2013)
Singh, S., Gupta, A., Efros, A.A.: Unsupervised discovery of mid-level discriminative patches. In: European Conference on Computer Vision, pp. 73–86 (2012)
Juneja, M., Vedaldi, A., Jawahar, C.V., Zisserman, A.: Blocks that shout: distinctive parts for scene classification. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 923–930. Portland, OR (2013)
Endres, I., Shih, K.J., Jiaa, J., Hoiem, D.: Learning collections of part models for object recognition. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 939–946. Portland, OR (2013)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection, In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. San Diego, CA, USA (2005)
Malisiewicz, T., Gupta, A., Efros, A.A.: Ensemble of exemplar-SVMs for object detection and beyond, In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 89-96. Barcelona (2011)
Gu, C., Arbeláez, P., Lin, Y., Yu, K., Malik, J.: Multi-component models for object detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 445–458. Springer, Heidelberg (2012)
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 29(9), 1627–1645 (2010)
Hariharan, B., Malik, J., Ramanan, D.: Discriminative decorrelation for clustering and classification. In: European Conference on Computer Vision, pp. 459-472 (2012)
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Kuang, X., Sang, N., Chen, F., Gao, C., Wang, R. (2015). Mixture Models for Object Detection. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_32
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DOI: https://doi.org/10.1007/978-3-662-48558-3_32
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