Multimedia data mining using deep learning

Abstract

Due to the large amounts of Multimedia data on the Internet, Multimedia mining has become a very active area of research. Multimedia mining is a form of data mining. Data mining uses algorithms to segment data to identify useful patterns and to make predictions. Despite the successes in many areas, data mining remains a challenging task. In the past, multimedia mining was one of the fields where the results were often not satisfactory. Multimedia Data Mining extracts relevant data from multimedia files such as audio, video and still images to perform similarity searches, identify associations, entity resolution and for classification. As the mining techniques have matured, new techniques were developed. A lot of progress has been made in areas such as visual data mining and natural language processing using deep learning techniques. Deep learning is a branch of machine learning and has been used among other on Smartphones for face recognition and voice commands. Deep learners are a type of artificial neural networks with multiple data processing layers that learn representations by increasing the level of abstraction from one layer to the next. These methods have improved the state-of-the-art in multimedia mining, in speech recognition, visual object recognition, natural language processing and other areas such as genome mining and predicting the efficacy of drug molecules. This paper describes some of the deep learning techniques that have been used in recent research for multimedia data mining.