Abstract
Multi-class object detection is a promising approach for reducing the processing time of object recognition tasks. Recently, random Hough forests have been successfully used for single-class object detection. In this paper, we present an extension of random Hough forests for the purpose of multi-class object detection and propose local histograms of visual words as appropriate features. Experimental results for the Caltech-101 test set demonstrate that the performance of the proposed approach is almost as good as the performance of a single-class object detector, even when detecting a large number of 24 object classes at a time.
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Bosch, A., Zisserman, A., Muoz, X.: Image Classification using Random Forests and Ferns. In: Proc. of the IEEE Int. Conf. on Computer Vision, pp. 1–8 (2007)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88(2), 303–338 (2010)
Fanelli, G., Gall, J., Van Gool, L.: Hough Transform-based Mouth Localization for Audio-Visual Speech Recognition. In: Proc. of the British Mach. Vis. Conf. (2009)
Fei-Fei, L., Fergus, R., Perona, P.: Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. Computer Vision and Image Understanding 106(1), 59–70 (2007)
Gall, J., Lempitsky, V.: Class-Specific Hough Forests for Object Detection. In: Proc. of the IEEE Conf. on Comp. Vis. and Pat. Recog., pp. 1022–1029 (2009)
Jiang, Y.G., Ngo, C.W., Yang, J.: Towards Optimal Bag-of-Features for Object Categorization and Semantic Video Retrieval. In: Proc. of the ACM Int. Conference on Image and Video Retrieval, pp. 494–501 (2007)
Kumar, V., Patras, I.: A Discriminative Voting Scheme for Object Detection using Hough Forests. In: Proc. of the British Machine Vision Conference Postgraduate Workshop, pp. 1–10 (2010)
Leibe, B., Leonardis, A., Schiele, B.: Robust Object Detection with Interleaved Categorization and Segmentation. Int. J. of Comp. Vis. 77(1-3), 259–289 (2008)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Maji, S., Malik, J.: Object Detection Using a Max-Margin Hough Transform. In: Proc. of the IEEE Conf. on Comp. Vis. and Pattern Recog., pp. 1038–1045 (2009)
Mühling, M., Ewerth, R., Freisleben, B.: Improving Semantic Video Retrieval via Object-Based Features. In: Proc. of the IEEE Int. Conference on Semantic Computing, pp. 109–115 (2009)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing Visual Features for Multiclass and Multiview Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(5), 854–869 (2007)
Vedaldi, A., Fulkerson, B.: VLFeat: An Open and Portable Library of Computer Vision Algorithms (2008), http://www.vlfeat.org/
Yao, A., Gall, J., Van Gool, L.: A Hough Transform-Based Voting Framework for Action Recognition. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2061–2068 (2010)
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Mühling, M., Ewerth, R., Shi, B., Freisleben, B. (2011). Multi-class Object Detection with Hough Forests Using Local Histograms of Visual Words. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_47
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DOI: https://doi.org/10.1007/978-3-642-23672-3_47
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