Abstract
Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.
TopBackground
Multimedia data mining has been performed for different types of multimedia data: image, audio and video. Let us first consider image processing before discussing image and video data mining since they are related. Image processing has been around for some time. Images include maps, geological structures, biological structures, and many other entities. We have image processing applications in various domains including medical imaging for cancer detection, and processing satellite images for space and intelligence applications. Image processing has dealt with areas such as detecting abnormal patterns that deviate from the norm, and retrieving images by content (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). The questions here are: what is image data mining and how does it differ from image processing? We can say that while image processing focuses on manipulating and analyzing images, image data mining is about finding useful patterns. Therefore, image data mining deals with making associations between different images from large image databases. One area of researches for image data mining is to detect unusual features. Its approach is to develop templates that generate several rules about the images, and apply the data mining tools to see if unusual patterns can be obtained. Note that detecting unusual patterns is not the only outcome of image mining; that is just the beginning. Since image data mining is an immature technology, researchers are continuing to develop techniques to classify, cluster, and associate images (Goh, Chang, & Cheng, 2001; Li, Li, Zhu, & Ogihara, 2002; Hsu, Dai, & Lee, 2003; Yanai, 2003; Müller & Pun, 2004). Image data mining is an area with applications in numerous domains including space, medicine, intelligence, and geoscience.
Mining video data is even more complicated than mining still image data. One can regard a video as a collection of related still images, but a video is a lot more than just an image collection. Video data management has been the subject of many studies. The important areas include developing query and retrieval techniques for video databases (Aref, Hammad, Catlin, Ilyas, Ghanem, Elmagarmid, & Marzouk, 2003). The question we ask yet again is what is the difference between video information retrieval and video mining? There is no clear-cut answer for this question yet. To be consistent with our terminology, we can say that finding correlations and patterns previously unknown from large video databases is video data mining.