Academic literature on the topic 'Multimedia Data Mining'

With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.

With the extensive use of smart devices and blooming popularity of social media websites such as Flickr, YouTube, Twitter, and Facebook, we have witnessed an explosion of multimedia data. The amount of data nowadays is formidable without effective big data technologies. It is well-acknowledged that multimedia high-level semantic concept mining and retrieval has become an important research topic; while the semantic gap (i.e., the gap between the low-level features and high-level concepts) makes it even more challenging. To address these challenges, it requires the joint research efforts from both big data mining and multimedia areas. In particular, the correlations among the classes can provide important context cues to help bridge the semantic gap. However, correlation discovery is computationally expensive due to the huge amount of data. In this paper, a novel multimedia big data mining system based on the MapReduce framework is proposed to discover negative correlations for semantic concept mining and retrieval. Furthermore, the proposed multimedia big data mining system consists of a big data processing platform with Mesos for efficient resource management and with Cassandra for handling data across multiple data centers. Experimental results on the TRECVID benchmark datasets demonstrate the feasibility and the effectiveness of the proposed multimedia big data mining system with negative correlation discovery for semantic concept mining and retrieval.

Several advances have been made on managing multimedia databases as well as on data mining. Recently there is active research on mining multimedia databases. This paper provides an overview of managing multimedia databases and then describes issues on mining multimedia databases. In particular mining text, image, audio and video data are discussed.