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Real-Time Object Recognition Using Local Features  

Kim, Dae-Hoon (고려대학교 전자전기공학과)
Hwang, Een-Jun (고려대학교 전기전자전파공학과)
Publication Information
Journal of IKEEE / v.14, no.3, 2010 , pp. 224-231 More about this Journal
Abstract
Automatic detection of objects in images has been one of core challenges in the areas such as computer vision and pattern analysis. Especially, with the recent deployment of personal mobile devices such as smart phone, such technology is required to be transported to them. Usually, these smart phone users are equipped with devices such as camera, GPS, and gyroscope and provide various services through user-friendly interface. However, the smart phones fail to give excellent performance due to limited system resources. In this paper, we propose a new scheme to improve object recognition performance based on pre-computation and simple local features. In the pre-processing, we first find several representative parts from similar type objects and classify them. In addition, we extract features from each classified part and train them using regression functions. For a given query image, we first find candidate representative parts and compare them with trained information to recognize objects. Through experiments, we have shown that our proposed scheme can achieve resonable performance.
Keywords
Object recognition; Local features; Object classification; Real-time;
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1 T. Darrel, P. Indyk and G. Shakhnarovich, "Locality-sensitive hashing scheme based on p-stable distributions," Nearest Neighbor Methods in Learning and Vision: Theory and Practice, MIT Press, 2006.
2 D. N. Bhat and S. K. Nayar, "Ordinal Measures for Image Correspondence," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 4, pp. 415-423, 1998.   DOI   ScienceOn
3 L. Fei-Fei, R. Fergus and P. Perona, "Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories," in Workshop on Generative-Model Based Vision, 2004.
4 H. Zhang, A. Berg, M. Maire, and J. Malik, "SVM-KNN: Discriminative Nearset Neighbor Classification for Visual Category Recognition," in CVPR, 2006.
5 K. Grauman and T. Darrell, "Pyramic match kernels: Discriminative classficiation with sets of image features (version 2)," Tech. Rep. MIT CSAIL TR 2006-020, MIT, March 2006.
6 A. Frome and Y. Singer, "Image Retrieval and Classification Using Local Distance Functions," in NIPS 2006.
7 Y. LeCun, P. Haffner, L. Bottou and Y. Bengio, "Object recognition with gradient-based learning," in Feature Grouping, D. Forsyth, Ed., 1999.
8 H. Schneiderman and T. Kanade, "A statistical method for 3D object detection applied to faces and cars," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 746-751, 2000.
9 E. Borenstein and S. Ullman, "Combined Top-Down/Bottom-Up Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 12, pp. 2109-2125, 2008.   DOI
10 P. Viola and Michael J. Jones, "Robust Real-time Object Detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.   DOI
11 M. Ulrich, C. Steger and A. Baumgartner, "Real-time object recognition using a modified generalized Hough transform," International Journal of Pattern Recognition, vol. 36, no. 11, pp. 2557-2570, 2003.   DOI   ScienceOn
12 J. Gausemeier, J. Fruend, C. Matysczok, B. Bruederlin and D. Beier, "Development of a real time image based object recognition method for mobile AR-devices," in Proceedings of the 2nd international conference Computer graphics, virtual Reality, visualization and interaction in Africa, pp. 133-139, 2003.
13 A. Mohan, C. Papageorgiou, and T. Poggio, "Example-based object detection in images by components," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no.4, pp. 349-361, 2001.   DOI   ScienceOn
14 S. Agarwal, A. Awan and D. Roth, "Learning to Detect Objects in Images via a Sparse, Part-Based Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1475-1490, 2004.   DOI   ScienceOn
15 T. Kadir and M. Brady, "Scale, Saliency and Image Description," International Journal of Computer Vision, vol. 45, no. 2, pp. 83-105, 2001.   DOI   ScienceOn
16 M. Weber, M. Welling and P. Perona, "Unsupervised learning of models for recognition," in Proceedings of the Sixth European Conference on Computer Vision, pp. 18-32, 2000.
17 R. M. Haralick and L. G. Shapiro, "Computer and Robot Vision II," Addison-Wesley, 1993.
18 S. Ullman, "High-level vision: object recognition and visual cognition," MIT Press, 1996.
19 H. A. Rowley, S. Baluja and T. Kanade, "Neural network based face detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, 1998.   DOI   ScienceOn
20 A. J. Colmenarez and T. S. Huang, "Face Detection With Information-Based Maximum Discrimination," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 782-787, 1997.
21 E. Osuna, R. Freund and F. Girosi, "Training support vector machines: an application to face detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 130-136, 1997.
22 M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.   DOI   ScienceOn
23 B. Moghaddam and A. Pentland, "Probabilistic visual learning for object detection," Proceedings of the Fifth International Conference on Computer Vision, 1995.
24 Y. Amit and D. Geman, "A computational model for visual selection," Neural Computation, vol. 11, no. 7, pp. 1691-1715, 1999.   DOI   ScienceOn
25 M-H. Yang, D. Roth and N. Ahuja, "A SNoW-based face detector," in Advances in Neural Information Processing Systems 12, Sara A. Solla, Todd K. Leen, and Klaus-Rober Muller. , Eds., pp. 855-861 , 2000.
26 P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001.
27 L. Shams and J. Spoeslstra, "Learning Gabor-based features for face detection," in Proceedings of World Congress in Neural Networks, International Neural Network Society, pp. 15-20, 1996.
28 C. Papageorgiou and T. Poggio, "A trainable system for object detection," International Journal of Computer Vision, vol. 38, no. 1, pp. 15-33, 2000.   DOI   ScienceOn