Chapter 3 (MDM) | Part II Theory and Techniques 39 © 2009 by Taylor & Francis Group, LLC Chapter 2 Feature and Knowledge Representation for Multimedia Data Introduction Before we study multimedia data mining, the very first issue we must resolve is how to represent multimedia data. While we can always represent the multimedia data in their original, raw formats (., imagery data in their original formats such as JPEG, TIFF, or even the raw matrix representation), due to the following two reasons, these original formats are considered as awkward representations in a multimedia data mining system, and thus are rarely used directly in any multimedia data mining applications. First, these original formats typically take much more space than necessary. This immediately poses two problems – more processing time and more storage space. Second and more importantly, these original formats are designed for best archiving the data (., for minimally losing the integrity of the data while at the same time for best saving the storage space), but not for best fulfilling the multimedia data mining purpose. Consequently, what these original formats have represented are just the data. On the other hand, for the multimedia data mining purpose, we intend to represent the multimedia data as useful information that would facilitate different processing and mining operations. For example, Figure (a) shows an image of a horse. For such an image, the original format is in JPEG and the actual “content” of this image is the binary numbers for each byte in the original representation which does not tell anything about what this image is. Ideally, we would expect the representation of this image as the useful information such as the way represented in Figure (b). This representation would make the multimedia data mining extremely easy and straightforward. However, this immediately poses a chicken-and-egg problem – the goal of the multimedia data mining is to discover the knowledge represented in an
