Clustering multimedia data using time series

After the generation of multimedia data turned digital, an explosion of interest in their data storage, retrieval, and processing has drastically increased. This includes videos, images, and audios, where we now have higher expectations in exploiting these data at hands. Typical manipulations are in some forms of video/image/audio processing, including automatic speech recognition, which require fairly large amount of storage and are computationally intensive. In this work, we will demonstrate how these multimedia data can be reduced to a more compact form, i.e., time series representation, without losing the features of interest. This approach can be extensively applied to a variety of applications and domains, such as object tracking in video data, image profiles recognition, classification, etc. We will demonstrate the utility of our approach in the task of clustering multimedia data using time series representation, pointing out a wide range of research and applications, including voice/face recognition, fingerprinting, and other types of biometric authentication, which allow considerable amount of reduction in computational effort and storage space.