Fast Pixel Classification by SVM Using Vector Quantization, Tabu Search and Hybrid Color Space

In this paper, a new learning method is proposed to build Support Vector Machines (SVM) Binary Decision Function (BDF) of reduced complexity, efficient generalization and using an adapted hybrid color space. The aim is to build a fast and efficient SVM classifier of pixels. The Vector Quantization (VQ) is used in our learning method to simplify the training set. This simplification step maps pixels of the training set to representative prototypes. A criterion is defined to evaluate the Decision Function Quality (DFQ) which blends recognition rate and complexity of a BDF. A model selection based on the selection of the simplification level, of a hybrid color space and of SVM hyperparameters is performed to optimize this DFQ. Search space for selecting the best model being huge. Our learning method uses Tabu Search (TS) metaheuritics to find a good sub-optimal model on tractable times. Experimental results show the efficiency of the method.