# Multilabel Genre Prediction Using Deep-Learning Frameworks
## 2 Related Work  
In the literature, the genre classification studies use different data types such as text, sound, images from a variety of sources like posters, fragments, and summaries. In this section, these studies will be summarized from a broad perspective. [PERSON] et al. [11] used movie trailers to determine movie genres. The support vector machine classifier was fed the visual and auditory features derived by the self-adaptive harmony search algorithm, which is a metaheuristic optimization algorithm. The genre of each film was determined using a majority voting system. The models were evaluated using the Apple Movie Trailers website and the IMDB dataset, which contains seven genres. The proposed model obtained a 91.9% accuracy rate. Using visual and auditory features, [PERSON] et al. [12] determined the content-based genre classification of TV programs and YouTube videos. A support vector machine (SVM) model was trained. On this model, the one-to-all method was used for the feature and genre variants. The final result was determined by combining the classifier's output. The information about color and texture was represented by six low-level visual features that were also used to detect high-level features. The YouTube dataset was used to validate the model, which achieved an accuracy rate of 87.3%. In their study, [PERSON] et al. [13] used movie posters and summaries to autonomously detect film genres. The posters collected from popular foreign film sites and the relevant summaries from the Movie Database (TDMB) were used and four genres were determined. Features such as color, edge, texture, and face detection were removed from movie posters. The vector space model was utilized to extract text features from summaries. Using these features, two distinct support vector machines were trained to produce a poster classifier and a text classifier. On the outputs of these classifiers, the testing phase based on the 'OR' operation was performed as a fusion operator. The proposed model achieved an accuracy rate of 88.05%. [PERSON] et al. [14] introduced a novel approach, CNN-MoTion, that utilized a convolutional neural network (CNN) model for the purpose of predicting movie genres from their respective trailers. A new dataset, denoted as LMTD (Labeled Movie Trailer Data), was generated, comprising over 3500 trailers that were categorized into four distinct genres, as per the study's parameters. The model's success rate, as determined by the accuracy metric, was 73.75%. The model under consideration exhibited a superior performance by approximately 5% in contrast to other well-known movie trailer classification techniques such as Gist, CENTRIST, and w-CENTRIST. [PERSON] et al. [15] introduced a deep neural network model which used visual appearance and object information for multilabel genre classification. Within the scope of the study, posters were taken using the IMDB dataset and 23 different genres were determined. AlexNet, a pretrained CNN model, was modified to extract visual features, and the YOLO method was used to detect objects on posters. It was determined that YOLO was effective in measuring the contribution of object detection to genre classification success. The proposed method achieved an L1 norm vector accuracy of 18.73%. [PERSON] et al. [16] proposed a deep-learning approach to predict movie genres from movie posters. To determine the optimal model for this classification problem, the transfer-learning technique was applied to contemporary pretrained models such as ResNet-50, VGG-16, and DenseNet-169. The final layers of the pretrained architectures were modified based on the nature of the problem. The Kaggle dataset was utilized and seven genres were defined to test the model. The DenseNet-169 model was determined to be the most successful based on its F1-score of 0.77 percent and ROC-AUC of 0.67 percent. [PERSON] et al. [17] presented a multimodal model, namely Gated Multimodal Unit (GMU), which utilizes gated neural networks to predict the genre of a movie based on its plot and poster.