A Novel Image Classification Algorithm Based on Graph Theory

In this paper the bag-of-words model is applied to image classification and improves the existing problems of the traditional bag-of-words method. We propose a method of combination of corner detection and graph theory for ROI region extraction and fuzzy membership degree. First using corner detection for images, then the ROI region is defined by the method of graph theory. Then the SIFT features of the ROI region are extracted and the visual dictionary is generated. The visual dictionary can be more accurate to describe the image features, which can reduce the influence of background information and other interference information. Secondly, the concept of fuzzy membership function and information of feature space is introduced to improve the image of the visual histogram. Finally, support vector machine classifier is used to classify. Through the experiment of the Caltech 100 database, the result shows that the method improves the accuracy of classification compared with the traditional method. Introduction Image classification is a fundamental problem in the field of computer vision. It plays a key role in many application scenarios, such as image analysis and visual monitoring. One of the most important methods is the bag-of-words model. In recent years, bag-of-words model has been widely applied in many common data sets and competitions, such as 15-scenes[1], Caltech 101 [2], Caltech 256 [3] and Image Net [4]. Traditional visual bag-of-words model is generally implemented by SIFT descriptor, k-means clustering method and SVM classifier. However, it is vulnerable to the interference of complex factors such as perspective, scale, background and so on, which makes the accuracy of image classification often not reach the optimal level. In order to solve these problems, scholars have proposed many improved algorithms for the bag-of-words model. Wang [5] and others proposed a spatial pyramid matching model, and Philbin [6] proposed a method to construct statistical histogram of visual vocabulary for soft assign. Scholars have found that when people identify images, they tend to be interested in a certain area rather than the whole image. Therefore, a new idea about the region of interest is proposed, which can reduce the interference of the feature points of the non-interest region by extracting the features of the region of interest. How to extract feature descriptors and generate image histogram representation is the focus of this paper. The main contributions of this paper are as follows: corner detection was performed on the original image, and the ROI area was determined by combining the graph theory method. Then, SIFT feature points were extracted from the ROI area. In this way, the extracted SIFT feature descriptors can be concentrated on the object and some interference points on the background can be eliminated. In the process of image histogram generation, fuzzy membership function and feature space information are introduced to study the influence of histograms generated by different membership functions on the classification effect. The Basic Framework of the Bag-of-Words Model Because of its simple and effective advantages, the bag-of-words model was first widely used in the field of text processing and then introduced into the field of image processing. Its basic principle is to regard the text as a set representation of disordered keywords. By counting the number of occurrences of each keyword in the text, a vector histogram of keywords is generated to represent the text. In the corresponding image classification field, the basic steps of bag-of-words model