## 3 Methodology and Materials
### MI for Feature Selection
In our study, _MI_ served as a crucial tool for feature selection in the context of scene recognition. Feature selection is a pivotal step in enhancing the efficiency and accuracy of scene recognition models. The primary aim of _MI_-based feature selection is to systematically evaluate the relevance of individual features extracted from the proposed deep multimodal architecture. These features originate from the first fully connected layer of both models, each representing distinctive characteristics of the input images. _MI_, in this paper, quantifies the degree of information shared between each feature and its correlation with scene labels. The investigation of several techniques for selecting features is a significant area of scholarly inquiry [26; 27]. For instance, [PERSON] et al. [26] tested three upgraded feature selection algorithms on numerous Chinese text categories. This article discusses CHMI, an improved version of the CHI square with _MI_ method that includes word frequency and term correction. TF-CHI adds phrase frequency to the weight calculation. Finally, the extreme gradient boosting (XGBoost) technique is added to the TF-IDF algorithm to improve word filtering. The empirical results in their work show that the feature selection algorithms improve performance across varied news corpora. However, each corpus type has different optimal feature selection strategies. In our work, the selection of _MI_ as the chosen method is justified based on its strong theoretical foundation, demonstrated empirical efficacy, and its suitability for the specific demands of scene recognition. It is crucial to acknowledge that the selection of _MI_ was not made in isolation but rather through a thorough evaluation of its advantages and its alignment with the research aims. The input for _MI_-based feature selection in our model consists of the extracted feature vector of the two models within the multimodal architecture. These features, derived from distinct modalities, collectively capture rich information about the input images. Additionally, the input includes scene labels that categorize these images into various scene classes. The output of the _MI_-based feature selection process is a refined feature set. This subset is composed of features with high _MI_ scores, indicating a substantial mutual dependency between these selected features and the scene labels. Notably, the _MI_-based feature selection includes an added layer after the fusion of the two models, responsible for selecting suitable features from both models and combining them into one vector. This combined feature vector is then integrated into the deep model to complete the feature selection process. To implement _MI_-based feature selection in our model, we adopt the following algorithmic steps:  
* As an initial step, we gather a dataset comprising feature vectors from the first fully connected layer of both models in our multimodal architecture. This dataset also includes the corresponding scene labels. * For each feature in the dataset, _MI_ is calculated with respect to the scene labels, following the _MI_ formula. Importantly, this _MI_ calculation takes place after the fusion of the two models. * Based on the computed _MI_ scores, we rank the features in descending order. Higher _MI_ scores signify stronger _MI_ content between selected features and scene labels. * The top N features with the highest _MI_ scores are chosen, considering factors such as model complexity and computational efficiency. These features form a feature subset. * An additional layer for _MI_ feature selection is incorporated after the fusion of the two models. This layer selects suitable features from both models and combines them into a unified feature vector. * The combined feature vector is integrated into the deep model, enhancing its feature representation for scene recognition tasks.