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Polygonal approximation is an effective yet challenging digital curve representation for image analysis, pattern recognition and computer vision. This paper proposes a novel approach, integer particle swarm optimization (iPSO), for polygonal approximation. When compared to the traditional binary version of particle swarm optimization (bPSO), the new iPSO directly uses an integer vector to represent the candidate solution and provides a more efficient and convenient means for solution processing. The velocity and position updating mechanisms in iPSO not only have clear physical meaning, but also guarantee the optimality of the solutions. The method is suitable for polygonal approximation which could otherwise be an intractable optimization problem. The proposed method has been tested on commonly used synthesized shapes and lake contours extracted from the maps of four famous lakes in the world. The experimental results show that the proposed iPSO has better solution quality and computational efficiency than the bPSO-based methods and better solution quality than the other state-of-the-art methods.

A novel shape description method is proposed for mobile retrieval of leaf images to aid in plant recognition. In this method, traveling the shape contour, the convexity and concavity properties of the arches of various levels are measured, respectively, to generate a multiscale shape descriptor. Its performance has been tested on two leaf datasets and the experimental results indicated higher recognition accuracies than the state-of-the-art approaches with a speed improvement of more than 170 times. The proposed method has been successfully applied to develop a prototype system of online plant leaf identification working on a consumer mobile platform.

Local binary patterns are a popular local texture feature for describing textures and objects. The standard method and many derivatives use a hand-crafted structure of point comparisons to encode the local texture to build the descriptors. In this paper we propose automatically learning a discriminative pattern structure from an extended pool of candidate pattern elements, without restricting the possible configurations. The learnt pattern structure may contain elements describing many different scales and gradient orientations that are not available in LBP (and related patterns), thus allowing the flexibility to construct structures capable of better representing the objects under test. We show through experimentation on two face recognition databases that this approach consistently outperforms other methods, in terms of training speed and recognition accuracy in every tested case.