用混沌映射重组局部图像特征：在纹理识别中的应用
Reorganizing local image features with chaotic maps: an application to texture recognition
摘要：
尽管最近卷积神经网络在纹理识别方面取得了成功，但基于模型的描述符仍然具有竞争力，特别是当我们无法访问大量带注释的数据进行训练时，模型的解释是一个重要问题。在基于模型的方法中，分形几何一直是最流行的方法之一，特别是在生物学应用中。然而，分形是一个更广泛的模型家族的一部分，这些模型是混沌理论中研究的非线性算子。在这种情况下，我们提出了一个基于混沌的局部描述符纹理识别。更具体地说，我们将图像映射到三维欧氏空间，在这个三维结构上映射一个混沌映射，并将其转换回原始图像。在每次迭代中，我们从这样的混沌变换图像中收集局部描述符（这里我们使用局部二进制模式），这些描述符组成纹理的特征表示。我们的方法的性能进行了验证的基准数据库的分类，并在巴西植物物种的叶表面的纹理的基础上识别。所取得的成果证实了我们的期望有竞争力的性能，即使与一些学习为基础的现代方法在文献中。
英文摘要：
Despite the recent success of convolutional neural networks in texture recognition, model-based descriptors are still competitive, especially when we do not have access to large amounts of annotated data for training and the interpretation of the model is an important issue. Among the model-based approaches, fractal geometry has been one of the most popular, especially in biological applications. Nevertheless, fractals are part of a much broader family of models, which are the non-linear operators, studied in chaos theory. In this context, we propose here a chaos-based local descriptor for texture recognition. More specifically, we map the image into the three-dimensional Euclidean space, iterate a chaotic map over this three-dimensional structure and convert it back to the original image. From such chaos-transformed image at each iteration we collect local descriptors (here we use local binary patters) and those descriptors compose the feature representation of the texture. The performance of our method was verified on the classification of benchmark databases and in the identification of Brazilian plant species based on the texture of the leaf surface. The achieved results confirmed our expectation of a competitive performance, even when compared with some learning-based modern approaches in the literature.
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