# The Study of Multiple Classes Boosting Classification Method Based on Local Similarity
###### Abstract
Intuitively, when we want to combine the judgments of each weak learner into the final prediction result, if the weak learner performs well in the previous task, we will believe it more, on the contrary, if the weak learner in the previous task performs poorly, we believe it less. In other words, we will combine weak learners in a weighted manner, and assign each weak learner a value indicating the degree of credibility. This value depends on its performance in the assigned task. The better the performance, the greater the value, and vice versa. AdaBoost training will eventually have two situations: one is that the training error is reduced to zero. The other is that no matter how many times of training, there will be no overfitting problem. Each subsequent weak classifier has an accuracy of 0.5. However, these are limited to the same mode. If you want to better apply it in real-life, it is a more cross-modal retrieval application. Each source or form of information can be called a mode. For example, people have a sense of touch, hearing, sight, and smell; the medium of information includes voice; video; text; and a variety of sensors, such as radar, infrared, and accelerometer. Each of the above can be called a mode. The main method of cross-modal retrieval is to find the relationship between different modes and map different modes to the same subspace, where we can measure the similarity between different modes. Specifically, first, we have some training real columns, each instance is an image-text pair with a label; then, these real columns are divided into train set and test set, there are unrelated image-text pairs in each set, but all have the same category (that is, the training set and the test set do not have the same image-text pair, but the category is the same); then, during training, we learn a common semantic space from the train set; then, apply the common semantic space to the test set to generate a common feature representation for the instances in the test set; finally, use the common feature representation to compare The degree of similarity of the samples of the two modes is thus matched. Eventually, stronger learners of each modal can generate semantic vectors and apply logistic regression to obtain semantic vectors of images and texts. However, when features have high dimensions and sparse values, logistic regression cannot work effectively. Although image texts essentially represent the same set of semantic concepts, their manifestations differ greatly due to the large differences between different data modes. How to robustly represent the similarity between image and text and accurately measure the similarity is a tricky problem. To deal with this problem, the existing methods can be divided into two main categories according to the different ways of modeling the corresponding relationship between image and text: one-to-one matching and many-to-many matching. As shown in Figure 1A, we can see that it is a one-to-one matching method, usually extracting global feature representations of images and texts, such as desks, chairs, computers, illustrations, etc. In the picture, and then using the structure, the objective function of transformation or canonical correlation analysis projects their features into a common space, so that the distance between similar pairs of image texts in the space is close, that is, the similarity is high. However, this matching method is only a rough measure of the global similarity of image text and does not specifically consider which local content of the image text is semantically similar. Therefore, in some tasks that require accurate similarity measurement, such as fine-grained for cross-modal retrieval, the experimental accuracy is often low. In Figure 1B, the many-to-many matching method is shown. The many-to-many matching method is to try to extract multiple local instances from the image text, and then measure the local similarity for multiple paired instances. Fusion results in global similarity.