Object detection by color histogram-based fuzzy classifier with support vector learning

A new method for specific object detection in two-dimensional color images is proposed in this paper. The proposed method uses color histograms of an object on the hue and saturation (HS) color space as detection features. To represent color information by histograms as accurately as possible, a non-uniform partition of HS space is proposed. The whole 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 fuzzy classifier. The fuzzy classifier used is a self-organizing Takagi-Sugeno (TS)-type fuzzy network with support vector learning (SOTFN-SV). SOTFN-SV is a fuzzy system constituted by TS-type fuzzy if-then rules. It is constructed by the hybridization of fuzzy clustering and support vector machine. Many candidate objects are detected in this stage. In the second stage, a splitting K-means clustering method is proposed and applied to the detections from Stage 1 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. Final object position is determined from the clusters. In the final stage, size of a detected object is determined. To verify performance of the proposed method, experiments on five specific object detections are conducted and comparisons with different types of detectors are made.