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Top1. Introduction
The development and advancement of digital recording techniques, communication platforms, and storage systems have made it much easier for people to access, collect, share, and distribute multimedia data in various services and applications such as entertainment, distant education, e-commerce, social networks, homeland security, surveillance, and medicine. Multimedia services and applications enable a simple terminal unit (like a cell phone or computer screen) to utilize the multimedia data. In addition, multimedia systems can also provide convenience for terminal users. For example, many users benefit from the utilization of intelligent multimedia applications such as digital libraries, image or music search engines, movie or game recommenders, sports or news highlighters, and personalized picture or video collection sites.
At the same time, the amounts of multimedia data have also increased tremendously in the recent years, measuring from gigabytes (GB) to terabytes (TB). Regardless of which data model or which storage device is used, the most critical functionality of a multimedia database or a multimedia system is to provide effective and efficient search and retrieval of multimedia data with a short real-time constraint whenever applicable. The advanced database and data warehouse technologies enable the management of multimedia data, and the traditional keyword-based search and/or retrieval frameworks allow the users to query the data on demand. However, it does not work well since it requires heavy human efforts for annotation, indexing, browsing, and performance evaluation of the retrieved results. This calls for the development of the content-based techniques to effectively reduce manual efforts in multimedia indexing, to efficiently search the data from the multimedia database, and to automatically retrieve accurate and meaningful information from the data (Chen, Zhang, Chen, & Chen, 2005; Chen, Rubin, Shyu, & Zhang, 2006; Huang, Chen, Shyu, & Zhang, 2002; Shyu et al., 2003; Shyu, Chen, Chen, & Zhang, 2004; Zhang et al., 2005).
Differing from keyword-based search technologies, content-based video concept classification and retrieval approaches automatically extract feature data and provide more powerful search abilities for semantics (Chen, Rubin, Shyu, & Zhang, 2006; Chen, Zhang, Chen, & Rubin, 2009; Chen, 2010; Datta, Joshi, Li, & Wang, 2008; Jiang, Yang, Ngo, & Hauptmann, 2010; Lew, Sebe, Djeraba, & Jain, 2006; Liu, Weng, Tseng, Chuang, & Chen, 2008; Shyu, Chen, Sun, & Yu, 2007; Snoek & Worring, 2008). Though many programs have been developed with complex mathematical algorithms to allow the statistical analysis of media data and search, they become inefficient and difficult when dealing with large amounts of data, and there is a lack of true semantics of the multimedia data. Therefore, researchers are becoming increasingly interested in exploring multimedia data mining for retrieval since data mining is an important tool for transforming raw data into useful information and patterns (Witten, Frank, & Hall, 2011).