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Color Histogram-based Object Detection by Support Vector trained Fuzzy Systems
|關鍵字:||object detection;物體偵測;color histogram;splitting K-means clustering;fuzzy system;彩色直方圖;分裂的K-means分群法;模糊系統||出版社:||電機工程學系所||引用:||References  Z. Suna , G. Bebisa, R. Millerb, “Object detection using feature subset selection”, Pattern Recognition 37 (2004), pp. 2165- 2176  H. Zhang, W. Gao, X. Chen, D. Zhao, “Object detection using spatial histogram features”, Image and Vision Computing 24 (2006), pp. 327-341  Kumar S. Ray, J. Ghoshal, “Neuro-fuzzy reasoning for occluded object recognition”, Fuzzy sets and systems 94 (1998), pp. 1-28  T. kawanishi, T. Kurozumi, K. Kashino, S. Takagi, ”A fast template matching algorithm with adaptive skipping using inner-subtemplate's distances”, IEEE CNF. Pattern Recognition (ICPR'04), vol. 3, pp. 654-657, Aug. 2004.  D. I. Barnea and H. F. Silverman. “A class of algorithms for fast digital image registration”. IEEE Trans. on Comput., C-21(2):179-186, 1972.  T. Kawanishi, T. Kurozumi, S. Takagi, and K. Kashino. “Skipping template matching guaranteeing same accuracy as exhaustive search”. In Proc. of the Fifth Intl. Conf. of Advances on Pattern Recognition, pp. 209-212, 2003.  V. V. VINOD, H. MURASE, ”Focused color intersection with efficient searching for object extraction”, Pattern Recognition, Vol. 30, No. 10, pp.1787-1797, 1997.  Schiele, B. Crowley, J.L “Object recognition using multidimensional receptive field histograms”, IEEE CNF. Digital Object Identifier, Vol. 2, pp. 50-54, Aug, 1996.  Swain, M.J. and D.H. Ballard, “Color indexing”, International Journal of Computer Vision 7(1), pp. 11-32, 1991.  H. A. Rowley, S. Baluja, T. Kanade, “Neural Network-Based Face Detection”, IEEE Transactions on pattern analysis and machine intelligence. Vol. 20, pp. 22-38, January, 1998  N. Cristianini and J. S.-Taylor, An Introduction to Support Vector Machines And Other Kernel-based Learning Methods, Cambridge University Press, 2000.  J. H. Chiang, and P. Y. Hao, “Support vector learning mechanism for fuzzy rule-based modeling: a new approach,” IEEE Trans. Fuzzy Systems, vol. 12, no. 1, pp. 1-12, Feb. 2004.  Y. Chen, and J. Z. Wang, “Support vector learning for fuzzy rule-based classification systems,” IEEE Trans. Fuzzy Systems, vol. 11, no. 6, pp. 716-728, Dec. 2003.  C. T. Lin, C. M. Yeh, and C. F. Hsu, “Fuzzy neural network classification using support vector machine,” Prof. IEEE Symp. Circuits and Systems, pp. 724-727, 2004.  C. T. Lin, C. M. Yeh, S. F. Liang, J. F. Chung, and N. Kumar, “Support-vector- based fuzzy neural network for pattern classification,” IEEE Trans. Fuzzy Systems, vol. 14, no. 1, pp. 31-41, Feb. 2006.  T. Joachims, N. Cristianini, and J. Shawe-Taylor, “Composite kernels for hypertext categorization,” Proc. Int. Conf. Machine Learning, 2001.  J. S. Roger Jang, and C. T. Sun, “Functional Equivalence Between Radial Basis Function Networks and Fuzzy Inference System, IEEE Trans. Neural Networks, vol. 4, no. 1, pp. 156-159, January 1993.  S. H. Chiu, Skin Color Image Segmentation by Support Vector Machine-aided Self Organizing Fuzzy Network, Master Thesis, Department of Electrical Engineering, National Chung Hsing University, Taiwan, July, 2005.  C. F. Juang, and C. T. Lin, “An online self-constructing neural fuzzy inference network and its applications,” IEEE Trans. Fuzzy Systems, vol. 6, pp. 12-32, February 1998.||摘要:||
A new method for object detection by a Self-Organizing TS-type Fuzzy Network with Support Vector learning (SOTFN-SV) is proposed in this thesis. SOTFN-SV is a fuzzy system constructed by the hybridization of fuzzy clustering and SVM. The antecedent part of SOTFN-SV is generated via fuzzy clustering of the input data, and then SVM is used to tune the consequent part parameters to give the network better generalization performance. Object detection is based on color information and color histograms of an object on the Hue and Saturation (HS) color space are used as detection features. To represent color information by histograms as accurately as possible, a non-uniform partition of HS space is proposed. The whole histogram-based SOTFN-SV detection process consists of three stages. In the first stage, the input image is repeatedly sub-sampled by a factor, resulting in a pyramid of images. Scanning on all of the scaled images with a pre-defined window size is performed, where histograms of each window are fed as inputs to a SOTFN-SV classifier. Many candidate objects are detected in this stage. In the second stage, splitting K-means clustering is applied to the detections so that detections with nearby positions are grouped into the same cluster. The number of clusters is generated automatically by the clustering method according to cluster variances. The clusters containing detection numbers larger than a threshold are preserved and their centers are the positions of finally detected objects. In the final stage, size of each detected object is determined. To verify performance of the proposed method, experiments on cup detection are performed. For comparison, other three detectors, including histogram intersection, Self-cOnstructing Neural Fuzzy Inference Network (SONFIN), and Support Vector Machine (SVM), are applied to the same problem. From comparisons, we find that SOTFN-SV achieves the best detection result.
|Appears in Collections:||電機工程學系所|
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