Multimedia is primarily computer-driven interactive communication systems that create, store, transmit, and retrieve textual, graphic, and auditory information networks. In particular, Multimedia classification is a task that classifies given videos into specified classes. Machine learning is largely involved in multimedia applications of building models for classification and regression tasks, and the learning principle consists in designing the models based on the information contained in the multimedia dataset. Processing multimedia data is an emerging key area in applying machine learning techniques that provide insights into the domain to improve the performance of the data manipulating process. Many machine learning techniques can be used for the automatic classification of unprocessed multimedia data and to allow machines to learn features and perform specific tasks.
Learning principles for multimedia data in machine learning are Bayesian methods and Decision theory, Supervised learning, Unsupervised learning, Clustering, and Dimension reduction. Multimedia applications are Online Content-Based Image Retrieval Using Active Learning, Conservative Learning for Object Detectors, Machine Learning Techniques for Face Analysis, Mental Search in Image Datasets, Combining Textual and Visual Information for Semantic Labeling of Images and Videos, Machine Learning for Semi-structured Multimedia Documents, Classification and Clustering of Music for Novel Music Access Application. Smart city multimedia applications are transport management systems, healthcare systems, and surveillance systems, and Multimedia data has impacted a wide range of research areas, including multimedia retrieval, 3D tracking, database management, data mining, machine learning, social media analysis, and medical imaging.
• Nowadays, the development of intelligent systems deals with multimedia data efficiently and achieves stunning performances.
• In particular, the deep CNN model achieves promising performance for large-scale-high-dimensional multimedia classification techniques. It classifies the high-dimensional multimedia data by getting the advantages of the residual network.
• In addition to the quantity and dimensionality, the complexity and diversity of datasets are increasing day by day, and thus, the existing learning tasks are computationally inapplicable and incompetent for analyzing and modeling multimedia data.
• Due to the increasing availability of different multimedia data types, feature selection in multimedia becomes crucial in improving data classification accuracies and modeling the structured datasets.
• In recent years, the prevalence of Multimedia data enables a wide variety of multimodal applications such as image or video content analysis, multimedia search and recommendation, multimedia streaming, multimedia content delivery, and many others.
• AI enhances multimedia data classification based on the enrich deep neural networks with reasoning characteristics by utilizing other reasoning characteristics to augment deep neural networks.