Hypergraph-Based Locality Preserving Projection Algorithm And Its Application In Leaf Image Classification

It is estimated that there are about 400,000 kinds of plants around the world.Plant,as a kind of form of life,is the most widely distributed specie in the world,and it is the most closely related with humans and the environment.However,the current pollution problem has become more and more serious,resulting in a continuous decline in the number of plants,which has caused people's attention to plant protection.In a series of plant protection work,the classification of plants is the basis of completing these work.In other words,the classification of plants not only can help people to distinguish between plant species and master their distribution,but also can explore the genetic relationships between plants,and expound the evolution law of plant system.Most of the traditional plant classifications are done manually,this method is suitable for the classification of plants with apparent external characteristics and requires higher professional quality.In fact,ordinary people are unqualified for such job except botanists.In addition,this method often requires to collect a great number of samples and make a lot of specimen in the early stage,and involves long observation in the later stage,so it is not efficient.These disadvantages of artificial plant classification make the use of computers for automatic plant classification methods attract the attention of researchers.This method uses machine learning of computer science and image processing methods to achieve automatic plant classification tasks.However,the existing plant classification and identification methods usually use plant images as input,and classify plants by extracting some local characteristics of plants.This method is highly dependent on the extraction of local characteristics of plants.In fact,in the process of information acquisition,there are various types of noise.When images have very small size or images are very blurred,the extraction of local characteristics is often invalid.Because the extracted local characteristics are sensitive to noise,so existing automatic classification methods often cause wrong classification results.A hypergraph-based locality preserving projection algorithm is proposed in this paper.This method uses the hypergraph model based on the elastic network to represent the correlation of image data,and then uses the locality preserving projection algorithm to get a compact image representation for subsequent plant classification applications.Different from the existing automatic plant classification methods,this method starts with the plant image,and does not need to extract the local characteristics before the plant classification operation.Therefore,this method can not only eliminate the lack of information caused by the extraction of the local feature phase of the image,but also avoid the problem of poor plant characteristics because of the incomplete data,too small size,blurred picture and so on.In addition,this method uses the hypergraph model based on the elastic network to represent the correlation of the blade image.It can not only retain the high order relationship between different plant images,but also separate the noise components from the original data,making this method insensitive to noise and data damage,thus showing better results in the experiments.By classifying and identifying plants on two plant leaf datasets,it was shown that using this method can complete plant classification and identification tasks and obtain better plant classification results.