Discriminant WSRC for Large-Scale Plant Species Recognition

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Clustering multivariate and functional data using spatial rank functions

In this work, we consider the problem of determining the number of clusters in the multivariate and functional data, where the data are represented by a mixture model in which each component corresponds to a different cluster without any prior knowledge of the number of clusters. For the multivariate case, we propose a new forward search methodology based on spatial ranks. We also propose a modified algorithm based on the volume of central rank regions. Our numerical examples show that it produces the best results under elliptic symmetry and it outperforms the traditional forward search based on Mahalanobis distances. In addition, a new nonparametric multivariate clustering method based on different weighted spatial ranks (WSR) functions is proposed. The WSR are completely data-driven and easy to compute without any need to parameter estimates of the underlying distributions, which make them robust against distributional assumptions. We have considered parametric and nonparametric weights for comparison. We give some numerical examples based on both simulated and real datasets to illustrate the performance of the proposed method. Moreover, we propose two different clustering methods for functional data. The first method is an extension to the forward search based on functional spatial ranks (FSR) that we proposed for the multivariate case. In the second method, we extend the WSR method to the functional data analysis. The proposed weighted functional spatial ranks (WFSR) method is a filtering method based on FPCA. Comparison between the existing methods has been considered. The results showed that the two proposed methods give a competitive and quite reasonable clustering analysis