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Color Histogram-based Object Detection by Support Vector trained Fuzzy Systems
splitting K-means clustering
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Abstract 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.
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