Abstract
Radon transform based on complex shape detection is one of the most important challenges in the field of pattern recognition. Finding a robust transform for extracting and identifying complex shape is still an ongoing research issue. In this paper, aiming to simplify complex pattern recognition, we propose a new methodology of recognition flows. The proposed method is based on a decomposition of complex objects into elementary shapes and an evaluation of the pertinence of the generated primitives in recognizing the object. Accordingly, a series of the most pertinent primitives are selected. Then, the recognition step starts with a test on the most pertinent primitives. Our approach, called compound complex shape-invariant Radon transform, takes its importance in recognizing object class from a partial elementary scale and orientation-invariant Radon transforms, done on selected primitives. SVM classifier is then used for decision making based on obtained transforms. Validation of the proposed approach is done on the MPEG7 dataset, yielding a recognition rate of 98%.
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