Abstract
We propose a new method for object recognition in natural images. This method integrates bag of features model with efficient sub-window search technology. sPACT is introduces as local feature descriptor for recognition task. It can capture both local structures and global structures of an image patch efficiently by histogram of Census Transform. An efficient sub-window search method is adapted to perform localization. This method relies on a branch-and-bound scheme to find the global optimum of the quality function over all possible sub-windows. It requires much fewer classifier evaluations than the usually way does. The evaluation on PASCAL 2007 VOC dataset shows that this object recognition method has many advantages. It uses weakly supervised training method, yet has comparable localization performance to state-of-the-art algorithms. The feature descriptor can efficiently encode image patches, and localization method is fast without losing precision.
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References
Fergus, R., Perona, P., Zisserman, A.: A visual category filter for google images. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 242–256. Springer, Heidelberg (2004)
Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: 8th European Conference on Computer Vision, pp. 17–32. Springer, Heidelberg (2004)
Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: IEEE Computer Vision and Pattern Recognition 2005, pp. 10–17. IEEE Press, San Diego (2005)
Leordeanu, M., Heber, M., Sukthankar, R.: Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features. In: IEEE Computer Vision and Pattern Recognition 2007, pp. 1–8. IEEE Press, Minnesota (2007)
Shotton, J., Blake, A., Cipolla, R.: Contour-Based Learning for Object Detection. In: 10th International Conference on Computer Vision, pp. 503–510. IEEE Press, Beijing (2005)
Opelt, A., Pinz, A., Zisserman, A.: A Boundary-Fragment-Model for Object Detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 575–588. Springer, Heidelberg (2006)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Computer Vision and Pattern Recognition 2005, pp. 886–893. IEEE Press, San Diego (2005)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond Sliding Windows: Object Localization by Efficient Subwindow Search. In: IEEE Computer Vision and Pattern Recognition 2008, pp. 1–8. IEEE Press, Anchorage (2008)
Deng, H.L., Zhang, W., Mortensen, E.: Principal Curvature-Based Region Detector for Object Recognition. In: IEEE Computer Vision and Pattern Recognition 2007, pp. 1–8. IEEE Press, Minnesota (2007)
Ferrari, V., Fevrier, L., Jurie, F., Schmid, C.: Groups of Adjacent Contour Segments for Object Detection. IEEE Trans. Pattern Anal. Machine Intell. 30, 36–51 (2008)
Wu, J., James, M.R.: Where am I: Place instance and category recognition using spatial PACT. In: IEEE Computer Vision and Pattern Recognition 2008, pp. 1–8. IEEE Press, Anchorage (2008)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning Journal 63, 3–42 (2006)
Moosmann, F., Triggs, B., Jurie, F.: Fast Discriminative Visual Codebooks using Randomized Clustering Forests. In: Advances in Neural Information Processing Systems, vol. 19, pp. 985–992 (2006)
LIBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/cjlin/libsvm
PASCAL 2007 VOC dataset, The PASCAL Visual Object Classes Challenge (2007), http://www.pascal-network.org/challenges/VOC/voc2007/
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Nie, Q., Zhan, S., Li, W. (2009). Object Recognition Based on Efficient Sub-window Search. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_48
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DOI: https://doi.org/10.1007/978-3-642-05253-8_48
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