# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 1 Introduction  
Image classification is a crucial task of computer vision, whose goal is to divide an image into feature categories according to its feature information. Based on statistical learning theory, support vector machine (SVM) [1] is a kernel-based machine learning model that has shown promising classification results in fields such as pattern recognition, pattern classification, and computer vision [2]. However, traditional SVM has difficulty processing large amounts of image data and meeting the requirements of classification accuracy, and is therefore unsuitable for large and complex image classification. Deep learning (DL) learns features autonomously from images through neural networks, extracts abstract, high-dimensional features, and can tightly connect features to classifiers [3; 4; 5]. The convolutional neural network (CNN) is a widely used structure in DL models [6], with good results in image classification and recognition, such as of handwritten digits, where it has reached the accuracy of human vision, which was recognized in 2012 for the best image classification effect in the ImageNet Large-scale Visual Recognition Challenge [7]. With increasing advantages of neural networks in processing image features, traditional classification methods are combined with DL. SVM and softmax are two standard classifiers in computer vision, and with the improvement of CNN algorithms, the use of the two classifiers is increasing. Classification of extracted data is an essential aspect of DL. SVM can classify pre-extracted data and take the scores of data as a basis for evaluation [8; 9]. A linear SVM has replaced the softmax classifier in most DL models, and SVM has been used as the final classification unit of a CNN from feature extraction to classification, achieving better classification discrimination of L2-SVM over softmax on a DL image classificationdataset [3]. [PERSON] et al. [10], [PERSON] [11], and [PERSON] et al. [12] used a CNN to extract data features. They used SVM for feature extraction and classification, to somewhat better effect than a CNN. [PERSON] et al. [13] and [PERSON] et al. [14] combined deep neural networks with SVM in a hybrid algorithm to my in-depth features, which improved the classification or recognition accuracy of data. [PERSON] et al. [15] and [PERSON] et al. [16] fused an SVM classifier in a neural network with robust classification accuracy. SVMs and neural networks are used in the fields of image classification and recognition, such as human biology [17; 18; 19], medical imaging [11; 20; 21; 22; 23], and remote sensing [24; 25], performing better at complex tasks than original neural network models. The attention mechanism [26] is gaining importance in DL in image classification. [PERSON] et al. [27] integrated a recursive neural network into an attention mechanism to adaptively extract features in images, promoting large-scale image classification and recognition. Attention mechanisms have been widely used in computer vision and in areas such as finance, materials, meteorology, industry, and emotion detection [28]. In computer vision, these include the squeeze-and-excitation network (SENet) [29] and convolutional block attention module (CBAM) [30], but these cannot establish long-term dependencies on image classification or recognition tasks when capturing features. Although mechanisms such as GSoPNet [31], SCNet [32], and A2 Net [33] use non-local mechanisms to overcome the defects of SENet and CBAM, they have significant computational overhead. Coordinate attention (CA) [34] aggregates features in two spatial directions to capture position information and channel relations with low computational overhead, and achieves the feature representation of an enhanced network. SVM is an alternative to softmax classification, and fusing it with CNNs usually improves image classification accuracy, but features of lower levels do not adjust to the objectives of SVM.