## 1 Introduction  
Allergic diseases are common and are found in clinical practice [1]. They are listed as one of the three major diseases that need to be prevented and controlled in the 21 st century by the World Health Organization. Pollen allergens are one of the main causes and affect up to 30% of the population in industrialized countries [2]. Certain quantities of plant pollen allergens in the air can induce a series of allergic diseases, such as allergic rhinitis, bronchial asthma and dermatitis. These allergic diseases that are caused by plant pollen are also called hay fever. With the intensified urbanization of human society and the expansion of planting areas, the levels of pollen allergens have also increased. This has made pollen allergies become seasonal epidemic diseases that are accompanied by high incidence rates. In the United States, pollen allergies affect approximately 5% of the population and as much as 15% of the population in some regions. In Europe, they affect 20% of the population and it has been estimated that 30% of the population could be affected in the next 20 years [3]. In China, the incidence rate of pollen allergies is generally 0.5% to 1%, but it reaches 5% in high incidence areas [4]. This percentage has been increasing over recent years as well. Nearly 30% of allergic rhinitis cases in China are caused by pollen allergens. In Beijing, hay fever patients account for about 1/3 of all respiratory allergy patients. Effective pollen prediction can prompt hay fever patients to take positive countermeasures. Therefore, it is of great practical significance to identify and count the main allergenic pollen grains in the air. According to our survey, few pioneering works have made significant progress in this topic in China at present. As an international metropolis, the urban population in Beijing has shown an upward trend in pollen allergy incidence rate over recent years. As a result, the proportion of the population with pollen allergies is much higher than that in other cities. Considering this fact, we constructed an allergenic pollen dataset of pollen that is mainly found in the Beijing area and explored the automatic classification and identification of the allergenic pollen grains. As pollen grains vary greatly in size and shape, the identification of their morphological structures is a complex task [5]. Traditional pollen identification methods are generally realized by manually observing the surface characteristics of the pollen grains. In the manual identification process, experts need to possess the relevant knowledge and practical experience in pollen morphology. This process takes considerable amounts of time and resources, which limits research progress [6]. To solve these drawbacks, the automatic classification of pollen images has attracted more and more attention by utilizing computer vision and pattern recognition [7]. There are two main types of methods for the classification of pollen images that are based on computer vision: the traditional methods of manually extracting features and deep learning-based methods. The traditional methods rely on manually extracting features such as color, texture, shape and other features. The commonly used descriptors for texture features include the gray-level co-occurrence matrix (GLCM) [8], local binary patterns (LBPs) [9], log-Gabor filters (LGFs) [10] and discrete Tchebichef moments (DTMs) [11]. In [12], [PERSON] et al. artificially combined these four texture features and their classification accuracy reached 94.83% using a specific pollen dataset. In order to integrate richer pollen information, some studies have combined shape and texture features for pollen grain classification [7; 13; 14; 15]. For example, [PERSON] et al. [14] utilized geometric descriptors, first-order texture statistics and second-order GLCM-based texture statistics, which were obtained from the L\"a\"b\" color space. Their accuracy result could reach 95.6% using a 12-class pollen dataset.